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  • Sealed for 70 years, what BHU found inside 22 boxes from a Varanasi dig

    Sealed for 70 years, what BHU found inside 22 boxes from a Varanasi dig

    Why in the News

    Banaras Hindu University (BHU) opened 22 boxes on 20 August 2026 that had lain unopened for nearly 70 years, and found more than 10,000 artefacts, including painted black pottery believed to be nearly 3,000 years old.

    What is the Rajghat archaeological site?

    1. Location: Rajghat lies on the north-eastern edge of Varanasi, near the confluence of the Ganga and the Varuna rivers.
    2. Significance: It is one of the most important archaeological sites in the middle Ganga Valley.
    3. Historical identity: The area was part of ancient Varanasi, which served as the capital of the Kashi kingdom, also known as the Kashi Mahajanapad.
    4. Chronology: Archaeologists have divided the history of the site into six cultural periods, beginning around 800 BC and continuing up to the medieval period.

    What did the 22 boxes contain?

    1. The artefact count: The boxes held more than 10,000 artefacts in total.
    2. The dating anchor: Among them is painted black pottery believed to be nearly 3,000 years old.
    3. The object categories: The boxes contain sculptures, ancient coins, tools, stamp seals and objects made of copper, ivory and bone.
    4. A separate photographic archive: They also held around 3,000 glass slides and more than 5,000 photographic negatives documenting archaeological sites across India, including Rajghat, Manjhi, Ratnagiri, Ajanta and Ellora.
    5. Rare images within the archive: The photographs include rare images of idols of Vishnu, Shiva and Buddha, which are being examined and will be restored as part of the department’s conservation work.
    6. The condition of the containers: The trunks were found in an advanced state of deterioration, which makes the recovery and preservation of the material inside particularly significant.

    Why do the seals and inscriptions matter?

    1. Inscribed seals are present: Several of the seals recovered bear inscriptions, including some written in the Brahmi script.
    2. Who will read them: Researchers specialising in epigraphy at BHU will study them, and experts from other universities may be consulted where specialised expertise is required.
    3. What they can establish: The inscriptions can help researchers understand the scripts, systems of governance, and trade and economic systems of the periods in which they were used.
    4. Seals settled the site’s identity earlier: During the earlier excavations, seals and sealings bearing the name ‘Varanasi’ were found, and these findings helped establish the site’s connection with ancient Varanasi.

    Why did the material stay unstudied for 70 years?

    1. The site was found by accident: The first archaeological remains at Rajghat were discovered in 1939 during the expansion of the Kashi railway station, and were sent to the ASI for examination.
    2. The excavation ran for twelve years: Excavations were carried out jointly by the ASI and BHU between 1957 and 1969, under the supervision of BHU Professor A K Narain and the archaeologist T N Roy.
    3. What the excavations found: They unearthed remains of ancient settlements, including structures believed to be houses, terracotta objects, seals and other artefacts, which allowed researchers to trace the development of human settlement at Rajghat over several centuries.
    4. Documentation stopped short of study: Professor Narain documented the excavations in four volumes, and a large part of the material recovered was not studied in detail.
    5. The techniques did not exist then: The Vice-Chancellor noted that the objects were excavated at a time when advanced scientific techniques for archaeological research were not available in India.

    What happens to the material now?

    1. Three research teams have been formed: The department has constituted three specialised research teams to conserve and document the material.
    2. The stated purpose: The department has undertaken advanced research on its archaeological collections to allow scholars to reassess historical chronologies and produce new insights into ancient Indian civilisation.
    3. A parallel recovery is already under way: A month earlier, experts opened a box containing a human skeleton recovered from the same site, which had also remained sealed for around 70 years.
    4. Ancient DNA work has begun: A team specialising in ancient DNA collected samples from that skeleton for genetic and bio-archaeological analysis.
    5. What that analysis is expected to yield: The analysis is expected to provide clues about the people who lived in Varanasi around 1,000 years ago.

    “[2024] Consider the following information:

    Archaeological Site :: State :: Description

    1. Chandraketugarh : Odisha : Trading Port town

    2. Inamgaon : Maharashtra : Chalcolithic site

    3. Mangadu : Kerala : Megalithic site

    4. Salihundam : Andhra Pradesh : Rock-cut cave shrines

    In which of the above rows is the given information correctly matched?

    (a) 1 and 2 only

    (b) 2 and 3 only

    (c) 3 and 4

    (d) 1 and 4

  • The personalised vaccine that could cut skin cancer death risk

    The personalised vaccine that could cut skin cancer death risk

    Why in the News

    A new personalised cancer vaccine, intismeran, administered alongside the immunotherapy drug Keytruda, has been shown in Phase 3 results to reduce the risk of death from the recurrence and spread of skin cancer.

    How does intismeran work?

    1. Step one, read the tumour: The therapy begins by identifying the mutations, called neoantigens, in a sample of the patient’s own tumour.
    2. Step two, build the instruction set: A vaccine is then made of synthetically developed messenger RNA (mRNA), a single stranded molecule that carries genetic instructions from DNA in the cell nucleus and tells the cell which proteins to make. Each treatment consists of mRNA coding for 34 such neoantigens.
    3. Step three, administer and translate: Once administered, the body generates these proteins from the mRNA instructions.
    4. Step four, present to the immune system: The body then presents those proteins to the immune system, which is trained to recognise them as belonging to the cancer.

    Why must a cancer vaccine be personalised?

    1. Neoantigens exist only on cancer cells: Neoantigens are proteins found only on the cancerous cells, which the body’s immune system can be trained to recognise.
    2. They differ from patient to patient: These neoantigens vary from person to person, so they become an identifier for that individual’s cancer and cannot be mass produced as a single formulation.
    3. The principle is the same as any vaccine: A vaccine for an infectious disease contains the antigen from a pathogen, the proteins or lipids that train the immune system to recognise and fight it, and this therapy contains cancer neoantigens instead.
    4. The benefit is immunological memory: The cancer’s fingerprint enters the immune system’s memory, so if the cancer returns the body can recognise it immediately and mount a response, prolonging recurrence free survival.
    5. A decade of work behind one result: Work on this approach has run for around a decade, and this is the first clinical breakthrough.

    What did the Phase 3 study find?

    1. Death risk from recurrence fell: When the vaccine was given with Keytruda, the risk of death owing to recurrence of skin cancer went down by 49 per cent.
    2. Death risk from spread fell further: The risk of death owing to the cancer spreading went down by 59 per cent.
    3. The comparison arm matters: Both results are measured against treatment with Keytruda alone, not against no treatment.
    4. The comparison arm is already strong: Keytruda (pembrolizumab, a checkpoint inhibitor that blocks the PD-1 receptor cancer cells use to switch off the immune response against them) has over the years been shown to be much more effective in treating certain cancers than traditional chemotherapy, so the gain sits on top of an established benchmark.
    5. Side effects were mild: The most common side effects noted in the study were fatigue, injection site pain and chills.

    What does this mean for India?

    1. Reason one, the disease is rare here: Melanoma is one of the most common types of cancer in the caucasian population, and is not commonly seen among Indians.
    2. The share is a fraction of a per cent: Globocan, short for Global Cancer Observatory, an online platform that maintains cancer statistics, shows that melanoma accounts for only 0.26 per cent of all cancer cases in India and 0.17 per cent of deaths.
    3. Reason two, cost: Most patients in India are unable to afford Keytruda even with patient assistance programmes, and a combination therapy compounds a barrier that already exists for the immunotherapy alone.
    4. Access to immunotherapy is already narrow: A real world study from Tata Memorial Hospital showed that only 1.6 per cent of the patients who need such immunotherapy are able to access it.

    Challenges to personalised mRNA cancer vaccines

    1. Every dose is a separate manufacturing run: The vaccine must be sequenced, designed and produced per patient, so the process cannot be batched and the turnaround competes with tumour progression. Eg. Each treatment encodes 34 neoantigens specific to one person’s tumour. Fix. Build automated, closed-system manufacturing units co-located with cancer centres, on the model already used for cell therapy production.
    2. Cost scales with individualisation: A therapy that cannot be mass produced carries no volume discount, so the price gap over a standard drug widens rather than narrows with adoption. Eg. Even the standard companion immunotherapy reaches only 1.6 per cent of Indian patients who need it. Fix. Negotiate outcome linked pricing, where payment is tied to recurrence free survival achieved rather than to doses supplied.
    3. Cold chain requirements restrict reach: mRNA products require ultra-low temperature storage and transport, which most Indian district level oncology facilities do not have. Eg. Covid-19 mRNA vaccines were never widely deployed in India partly for this reason. Fix. Extend the cold chain built for the universal immunisation programme with ultra-low temperature capacity at regional cancer centres before such therapies are introduced.
    4. Tumours can escape the target: Cancer cells can lose the targeted antigen over time, which is the known failure mode of antigen directed immunotherapy. Eg. Relapse through antigen escape is documented in CAR-T cell therapy for blood cancers. Fix. Design vaccines against multiple conserved neoantigens and pair them with checkpoint inhibitors, so escape from one target does not end the response.
    5. Regulatory pathways assume a fixed product: Approval systems are built to assess an identical formulation across a trial population, while each dose here differs by design. Eg. India’s biotechnology approvals are already split across the Department of Biotechnology, the drug regulator and the environment ministry. Fix. Create a platform approval route that licenses the manufacturing process and the design algorithm rather than each individual product.
    6. The evidence is disease specific: The result is established for melanoma alone, and benefit in the cancers that dominate India’s burden is not demonstrated. Eg. Melanoma is 0.26 per cent of Indian cancer cases while breast, oral and cervical cancers account for the bulk. Fix. Prioritise Indian participation in trials of the same platform for oral, breast and cervical cancers, so approval evidence is generated on the local disease profile.

    Conclusion

    A personalised mRNA vaccine has for the first time produced a meaningful clinical benefit in cancer, cutting the risk of death from recurrence by 49 per cent and from spread by 59 per cent when added to an existing immunotherapy. The result validates the principle that a therapy can be built against each patient’s own tumour mutations rather than against a disease in general. For India the immediate impact is limited, because melanoma is rare here and the companion drug reaches under two per cent of the patients who need it. The question that remains open is whether the platform is extended to the cancers that actually dominate India’s disease burden.

    “[2022, GS3, 15 marks] What is the basic principle behind vaccine development? How do vaccines work? What approaches were adopted by the Indian vaccine manufacturers to produce COVID-19 vaccines?”

  • What India can learn from EU’s AI reset

    What India can learn from EU’s AI reset

    Why in the News

    The European Union’s Artificial Intelligence (AI) Omnibus entered into force on 27 July 2026 and changes parts of the European Union Artificial Intelligence Act, 2024 (EU AI Act). It extends some deadlines, simplifies some compliance requirements and gives regulators and companies more time to prepare for the high-risk AI rules.

    What is the EU AI Act’s risk-based framework?

    1. The organising principle: The Act sorts AI systems by the level of risk they pose and attaches obligations to each tier. The regulatory burden rises with the potential for harm rather than with the technology used.
    2. The prohibited tier: Some AI practices are prohibited outright under the Act. No compliance route is available for a practice in this category.
    3. The high-risk tier: High-risk systems face strict obligations before and during deployment. These are the obligations whose preparation deadlines the Omnibus has extended.
    4. General-purpose models: General-purpose AI models, meaning models trained broadly and adaptable to many downstream tasks rather than built for one application, came under a specific set of rules. They are governed separately from the risk tiers that apply to particular deployments.

    What does the AI Omnibus change, and why now?

    1. The instrument and its date: The AI Omnibus entered into force on 27 July 2026. It amends parts of the AI Act rather than replacing the framework.
    2. Deadlines extended: Some compliance deadlines under the Act have been pushed back. Regulators and companies have more time to prepare for the high-risk AI rules.
    3. Compliance simplified: Some compliance requirements have been simplified. The obligations themselves remain in place at their existing levels.
    4. The reason stated: Implementation of the original framework proved difficult in practice. The Omnibus is the EU’s response to that implementation experience rather than to a change in the risk assessment.
    5. How it is characterised: The change is an admission that AI is changing faster than laws can normally change. It demonstrates that even a carefully designed regulation must be capable of adjustment.

    What are the five lessons for India?

    1. Regulation must be capable of learning: Technology changes and risks change, so regulators must have the ability to review and adjust rules. Regulation should be treated as a continuing process rather than a single enactment.
    2. Regulation needs an escape valve: Rules work only where regulators and companies have the capacity to implement them. India should consider regulatory sandboxes and regular reviews of AI rules, and sunset mechanisms could make regulation more responsive.
    3. Compliance cost decides who can compete: Large technology companies can hire lawyers, engineers and auditors, and start-ups cannot always do so. Excessive compliance costs could unintentionally favour large companies and reduce competition.
    4. Simplification must not mean deregulation: Reducing paperwork is different from reducing safeguards. AI can create serious risks involving privacy, discrimination, manipulation and opaque decision-making, and simpler regulation must not mean weaker protection.
    5. Institutional maturity is the fifth lesson: The EU has shown that even a major regulatory framework can be revised after enactment. Regulatory maturity means recognising when rules are not working and changing them.

    Where does India’s AI governance currently stand?

    1. A different path so far: India has focused on responsible AI, innovation and sector-specific governance rather than creating a comprehensive AI law. Sectoral regulators apply existing mandates to AI within their own domains.
    2. Flexibility carries a cost: Flexibility can be useful and it should not become uncertainty. Businesses need clarity, citizens need protection and regulators need clear responsibilities.
    3. The proportionality principle India would need: The regulatory burden should depend on potential harm. The greater the risk to people and society, the stronger the safeguards should be.
    4. The assets India brings: India has a large digital population and experience with digital public infrastructure. It also has a growing technology sector and experience in deploying digital services at scale.
    5. The institutions available to build on: The IndiaAI Mission can play an important role in an adaptive Indian model of AI governance. Regulatory sandboxes, sectoral regulators, research institutions and industry bodies can carry the rest.

    Is regulation genuinely a trade-off against innovation?

    1. The framing the debate defaults to: The debate over AI is often presented as a choice between regulation and innovation. That framing treats every safeguard as a cost to be traded away.
    2. Why the framing is wrong: The choice is false because unregulated deployment carries its own costs in privacy, discrimination and opaque decision-making. The challenge is to design regulation that makes innovation safer and more trusted.
    3. What the EU revision actually demonstrates: The EU relaxed timelines and paperwork and did not relax the substantive safeguards. The revision therefore tests the trade-off framing and does not confirm it.
    4. The asymmetry the framing hides: Compliance cost falls hardest on the smallest firms, so heavy regulation reduces competition and light regulation reduces protection. India must create a framework that protects citizens while allowing experimentation, and be capable of changing as technology changes.

    Challenges to a risk-based AI law in India

    1. Risk tiers age faster than statutes: A fixed list of prohibited and high-risk uses is overtaken by capabilities that did not exist when the list was drawn. Eg. General-purpose models required a separate rule set in the EU Act after the original risk-tier design was settled. Fix. Place the risk classification in delegated rules subject to a mandatory periodic review rather than in the parent statute.
    2. Regulatory capacity is the binding constraint: Enforcement requires auditors and technical staff who can inspect model behaviour, and those skills are scarce in the public sector. Eg. Implementation difficulty is the stated reason the EU extended its own high-risk deadlines. Fix. Build a shared technical audit facility under the IndiaAI Safety Institute that sectoral regulators can draw on.
    3. Algorithmic bias reproduces existing exclusion: Models trained on historical data encode the patterns of that data, including patterns of discrimination. Eg. An automated recruitment system built at Amazon was found to downgrade applications from women. Fix. Mandate pre-deployment bias testing and published audit results for any system used in employment, credit or welfare decisions.
    4. The accountability gap in automated decisions: It is often unclear who is answerable for an AI-driven decision, the developer, the deployer or the administrator. Eg. A welfare eligibility system can deny a benefit without producing a reason the applicant can contest. Fix. Impose a statutory right to an explanation and to human review for any automated decision affecting a legal right or entitlement.
    5. Compute and data concentration: AI capability is concentrated in a few advanced economies, which leaves other countries as consumers rather than creators of the technology. Eg. India’s response has been a national compute grid of over 38,000 graphics processing units under the IndiaAI Mission. Fix. Treat compute, datasets and models as shared developmental resources with subsidised access for start-ups and researchers.

    Conclusion

    The EU has demonstrated that a comprehensive AI framework can be enacted and then revised when implementation shows it is not working, and the AI Omnibus of 27 July 2026 is that revision. Its lesson for India is not that regulation should be lighter but that it should be capable of learning, proportionate to harm, affordable for small firms and explicitly separate from deregulation. India has no comprehensive AI law and has the digital public infrastructure, the sectoral regulators and the IndiaAI Mission to build an adaptive one. What remains unresolved is whether India converts its current flexibility into a stated framework with clear responsibilities, or leaves it as uncertainty that businesses and citizens both bear.

    Government Initiatives on Artificial Intelligence

    1. IndiaAI Mission, 2024: Approved with an outlay of ₹10,371 crore and implemented by IndiaAI under the Ministry of Electronics and Information Technology. Its stated vision is making AI in India and making AI work for India, delivered through seven pillars.
    2. IndiaAI Compute and AIKosh: The compute pillar operates a national AI compute grid with over 38,000 graphics processing units at up to 40 per cent lower cost for eligible users. AIKosh is the national dataset repository with over 3,000 datasets and 243 models across 20 sectors.
    3. IndiaAI Foundation Models and FutureSkills: The foundation models pillar supports indigenous multimodal models built by entities including Sarvam AI and Gnani AI. FutureSkills funds fellowships and AI labs with a focus on Tier-2 and Tier-3 cities.
    4. Safe and Trusted AI: This pillar covers bias mitigation, privacy, explainability and AI governance, and it established the IndiaAI Safety Institute as a national trust framework. NITI Aayog’s Responsible AI for All initiative runs alongside it on public discourse and ethical audits.
    5. Language and access platforms: Digital India Bhashini provides speech and translation tools across 22 Indian languages, and Project Vaani has assembled a 150,000 hour Indian speech dataset. India hosted the India AI Impact Summit 2026 at Bharat Mandapam, the first major global AI summit in the Global South.

    “[2026] Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct?

    1. LLMs assign probabilities to the next possible words and then pick the one with the highest probability.

    2. LLMs process data through mathematical optimization to minimise prediction errors.

    3. LLMs produce unbiased outputs.

    (a) 1 only

    (b) 1 and 2 only

    (c) 2 and 3 only

    (d) 1, 2 and 3

  • What changes when AI moves from reading viral genomes to designing them?

    Why in the News

    Researchers at Stanford University and the Arc Institute used Artificial Intelligence (AI) to design complete genomes of bacteriophages, viruses that infect bacteria. Of 285 AI-generated designs physically synthesised and tested in the laboratory, 16 produced functioning phages, and some overcame bacterial resistance that had defeated the original virus. Humans have been synthesising viral genomes and deliberately modifying viruses for decades, so what is new is not the physical manufacture of a virus. AI has entered the design stage of biology, deciding what the genome should be rather than executing a design a human specified. The tension is that the same capability that could transform antimicrobial resistance research, vaccines and therapeutics could also accelerate harmful biological engineering.

    What is a genome language model?

    1. What it is: A genome language model is a machine learning system trained on genetic sequence data rather than on text, and the two used in this experiment were Evo 1 and Evo 2.
    2. How it works: The principle resembles a large language model, except that instead of learning patterns in words, it learns patterns in DNA.
    3. What it reads: It studies the genetic alphabet of A, C, G and T across vast numbers of genomes, and then generates new genetic sequences from the patterns it has learned.
    4. How it was specialised: For this experiment the models were further trained on thousands of bacteriophage genomes related to ΦX174, so the sequences they generated stayed within a known biological family.

    What did the Stanford-Arc experiment actually do?

    1. The design step was handed over: The scientists already knew the ΦX174 genome and already knew how to synthesise viral DNA and recover functioning phages. What changed was who, or what, proposed the genome.
    2. The output was constrained, not open-ended: AI did not invent a completely unrelated virus from nothing. It generated previously unseen ΦX174-like whole genomes within a known biological framework.
    3. The build step was conventional: Scientists selected some of these sequences, physically manufactured the DNA and introduced it into E. coli. Where the genetic instructions were biologically coherent, the bacterial machinery produced new phage particles.
    4. The yield: Of the 285 designs tested, 16 succeeded in producing functioning phages.
    5. Some designs beat the natural virus: Combinations of AI-designed phages overcame resistance in E. coli strains against which the original ΦX174 failed.
    6. The most significant result was combinatorial: An AI-designed phage successfully combined a viral protein with other genetic changes in a way conventional engineering had struggled to achieve, which suggests the system can identify multiple genetic changes that work together across an entire genome.

    How did biology get from reading genomes to writing them?

    1. Phages are old and abundant: Bacteriophages, literally “bacteria eaters”, have been known for more than a century and are among the most abundant biological entities in nature.
    2. Reading came first: In 1977, one particularly small phage, ΦX174, became the first complete DNA genome to be sequenced.
    3. Writing came next: By the early 2000s, scientists had shown that viral genetic material could be synthesised from known sequence information and used to recover functioning viruses.
    4. Deliberate modification followed: The controversial influenza gain-of-function experiments of 2011-12 showed that genetic changes could modify important properties such as transmission in experimental animals.
    5. The unresolved dilemma: That research highlighted a dilemma that remains open, since the same science that can improve pandemic preparedness may also create biosafety and biosecurity risks.
    6. Design is the fourth step: The progression runs from reading viral genomes, to writing them, to modifying them, and now to AI helping decide what should be written.

    What does this open up in medicine?

    1. Phage therapy is the nearest application: Antibiotic resistance is steadily eroding conventional treatment options, and bacteriophages offer another way of killing bacteria.
    2. Specificity is the limitation: A phage effective against one bacterial strain may fail against another, and bacteria can also develop resistance to phages.
    3. The search model has limits: Researchers have traditionally searched nature and phage libraries for suitable candidates, or modified existing viruses, which caps the available options at what already exists.
    4. The question changes: Generative biology moves medicine from asking whether the needed phage can be found to asking whether it can be designed.
    5. The applications extend well beyond phages: AI can assist the design of vaccine antigens, antibodies, therapeutic proteins and the viral vectors used to deliver genetic treatments, and may eventually help optimise oncolytic viruses that selectively attack cancer cells.
    6. The real shift is broader than viruses: The larger revolution is AI becoming capable of designing biological function, rather than AI making viruses.

    Is the simplicity of the target a safeguard, or is the risk the acceleration?

    1. The reassuring reading: ΦX174 is an exceptionally simple bacteriophage, while dangerous human viruses are vastly more complicated.
    2. Human pathogens are harder targets: They must negotiate receptor binding, host range, tissue tropism, replication, immune escape and transmission, each of which is a separate design problem.
    3. Complexity is not a defence: Human scientists already understand much about these determinants, and decades of virology, reverse genetics and gain-of-function research have linked many genetic changes to viral behaviour.
    4. AI does not need to rediscover virology: Its power lies in integrating what humanity already knows, examining vastly more combinations than humans can explore manually, and accelerating the path from hypothesis to experimental design.
    5. The concern is capability amplification: The relevant question is not whether an untrained individual can ask today’s chatbot to generate a pandemic virus. It is whether increasingly capable AI could make a knowledgeable and well-equipped laboratory substantially more effective at designing biological systems.
    6. A low success rate is a temporary comfort: The yield reported above is low, but digital systems can generate enormous numbers of candidates, so a low success rate is reassuring only while the number of attempts remains small.

    How must biosecurity change?

    1. Current screening looks for resemblance: Traditional DNA-synthesis screening often asks whether an ordered sequence resembles a known pathogen or toxin.
    2. Resemblance fails against generated sequences: A previously unseen sequence generated inside a known family may not resemble anything on a watchlist while still doing the same thing.
    3. Screening must move to function: In the age of generative biology, screening must also consider what a sequence might actually do, not simply whether it looks dangerous.
    4. Over-restriction has its own cost: Claude Fable 5 was initially deployed with strong safeguards around biology, chemistry and cybersecurity, and legitimate scientific work could sometimes trigger a fallback to a less capable model.
    5. The correction points to graduated access: Those safeguards have since been refined to reduce false-positive biology fallbacks while more sensitive capabilities remain restricted, which points toward graduated, auditable access under institutional and security controls.
    6. Model refusal is not a strategy: Biosecurity cannot rest entirely on what an AI model agrees or refuses to answer, so safeguards are needed throughout the chain: AI systems, DNA-synthesis providers, laboratories and institutional biosafety oversight.

    Why does this matter for India?

    1. Frontier AI becomes scientific infrastructure: If frontier AI becomes central to drug discovery, genomics, vaccines, protein engineering and experimental design, access to advanced AI becomes part of national scientific infrastructure.
    2. Sufficiency and compulsion are different things: Smaller and specialised models will be sufficient for many tasks, but a country should choose a small model because it is sufficient, not be forced to use one because somebody else owns the frontier.
    3. Restricted access compounds over time: If researchers elsewhere receive trusted access to highly capable biomedical models while Indian scientists depend on restricted public versions, the disadvantage accumulates across drug discovery, vaccines and antimicrobial resistance.
    4. The investment exists but needs a scientific arm: India is already investing through the IndiaAI Mission and indigenous foundation-model programmes, and that ambition should extend to scientific and biomedical AI, secure compute and high-quality datasets.
    5. Trusted access needs a framework: Legitimate researchers need a defined route to stronger capabilities, which requires an institutional trusted-access framework rather than case by case negotiation with model providers.
    6. The two goals are not separable: AI sovereignty without biosecurity would be reckless, and biosecurity without AI sovereignty could leave the country scientifically dependent.

    Challenges to AI-designed genomes

    1. Sequence screening cannot see intent: Order screening matches against known pathogen sequences, so a generated sequence within a benign-looking family passes even where its function is hazardous. Eg. Screening protocols built around named agents on an export control list match those names, so a functionally equivalent sequence outside the list is not flagged. Fix. Require DNA-synthesis providers to run function prediction alongside sequence matching, with a reporting duty on flagged orders.
    2. Automated laboratories compress the safety window: Combining generative design with robotic experimentation shortens the interval in which oversight can intervene. Eg. Future systems may compress months or years of literature review, modelling and experimental planning into much shorter cycles. Fix. Mandate institutional biosafety committee sign-off at the design stage rather than only before physical synthesis.
    3. Volume defeats low success rates: A weak per-attempt success rate becomes a strong aggregate capability once attempts are cheap and unlimited. Eg. The design pool in this experiment was generated computationally, so the number of candidates was bounded by compute rather than by laboratory effort. Fix. Impose volume-based reporting thresholds on synthesis orders from a single requester within a stated period.
    4. Model safeguards obstruct legitimate research: Blunt refusal policies block the research they were meant to protect, which pushes scientists toward unsupervised alternatives. Eg. Legitimate scientific queries triggered fallback to a less capable model under initial biology safeguards. Fix. Operate tiered credentials, where verified institutional researchers receive higher-capability access under audit logging.
    5. Governance is nationally fragmented: Biosecurity rules stop at borders while synthesis orders and model access do not. Eg. The 2011-12 gain-of-function controversy produced divergent national moratoria rather than a common standard. Fix. Negotiate a common minimum synthesis-screening standard through the Biological Weapons Convention review process.
    6. India lacks a biosecurity institution for generative biology: Existing oversight bodies were designed for genetically modified organisms and field trials, not for computational design of pathogens. Eg. The Genetic Engineering Appraisal Committee and the Review Committee on Genetic Manipulation are structured around organism release rather than sequence design. Fix. Create a statutory biosecurity review function covering generative design, synthesis orders and model access, reporting jointly to the Department of Biotechnology and the Ministry of Electronics and Information Technology.

    Conclusion

    The experiment does not show that AI can casually manufacture dangerous human viruses. It shows something more precise: computers are beginning to move from analysing biological information towards proposing biological designs that scientists can physically build, a capability that serves therapeutic research and harmful engineering alike. The answer is neither prohibition nor unrestricted access, but controlled acceleration, with safeguards rising as capability and risk rise. The unresolved question is no longer whether AI should be allowed to understand biology, but how to govern it once understanding biology becomes the ability to design it.

    Back2Basics: IndiaAI Mission

    1. What it is: The IndiaAI Mission is the national artificial intelligence programme approved in 2024 with an outlay of ₹10,371 crore, implemented by IndiaAI under the Ministry of Electronics and Information Technology.
    2. Its stated vision: “Making AI in India and Making AI Work for India”, built around seven pillars covering compute, applications, datasets, foundation models, skills, startup financing and safe and trusted AI.
    3. Compute pillar: It operates a national AI compute grid with over 38,000 graphics processing units, offering up to 40 per cent lower compute costs to eligible users.
    4. Safety arm: The IndiaAI Safety Institute is its national trust framework, covering bias mitigation, privacy, explainability and AI governance.

    Matching Previous Year Question

    “[2026] Which of the following statements with regard to genetic medicine is/are correct? 1. Genetic medicines correct/compensate for the faulty genes responsible for disease. 2. Engineered viruses and lipid nanoparticles are used as carriers of the genetic medicine. 3. Genetic medicines alter the entire DNA sequence. (a) 1 only (b) 2 and 3 only (c) 1 and 2 only (d) 1, 2 and 3 ANSWER: C”

  • Global space norms find a firm footing in India’s new re-entry rules

    Global space norms find a firm footing in India’s new re-entry rules

    Why in the News

    The Indian National Space Promotion and Authorisation Centre (IN-SPACe) has released India’s first guidelines on planned re-entry, requiring any Indian entity undertaking such a re-entry to obtain its authorisation, whether the re-entry occurs within or outside Indian territory.

    What is a planned re-entry?

    1. The defining test is intent and survivability: Objects designed to survive re-entry, or intentionally controlled towards a particular landing or impact area, require separate authorisation. This is what makes a re-entry planned.
    2. What falls outside the definition: Objects expected to burn up, melt or fragment sufficiently during natural orbital decay do not count as a planned re-entry.
    3. Why the distinction carries regulatory weight: The category separates a return that must be assessed and cleared in advance from one that requires no clearance, so the definition determines the reach of the entire framework.

    Why has re-entry become a governance problem now?

    1. The historical baseline was negligible: For many decades there were few rocket launches and few new satellites in orbit each year, so there were also few re-entries.
    2. The consequences used to be trivial: Most of those re-entries simply burned up in the atmosphere with little consequence.
    3. The orbital population has changed: Low-earth orbit, the band of orbits closest to the earth where most satellites operate, now hosts several thousand satellites, with private companies planning for many more.
    4. Deliberate de-orbiting has become routine: Satellite operators are also deliberately bringing satellites down at the end of their operational lives as part of post-mission disposal, in great numbers.
    5. The physical risks are specific: A spacecraft returning to the earth has to negotiate many risks, including deviating from its planned path and breaking up into smaller pieces.
    6. The risks cross jurisdictions: A returning object may affect airspace and maritime zones, and may potentially crash in the territory or jurisdiction of another state, which makes re-entry a governance problem as well as a physics problem.

    What are the three important elements of the guidelines?

    1. Accountability: Any Indian entity undertaking a planned re-entry, whether within or outside Indian territory, now requires IN-SPACe authorisation.
    2. Foreign operators must route through an Indian entity: Non-Indian entities seeking to undertake planned re-entry over Indian territory must route the activity through an Indian-incorporated entity, such as a subsidiary, joint venture or partnership.
    3. The Indian entity carries the compliance duty: That Indian entity is responsible for complying with Indian laws, regulations and national security requirements.
    4. Why the accountability gap exists: Commercialisation separates ownership from consequence, since the spacecraft may belong to a private company and the effects of its return lie across maritime zones and jurisdictions. India has responded by attaching regulatory responsibility to a re-entering entity before the risk materialises.
    5. Risk must be acceptable: The expected casualty risk must remain below 1 in 10,000, supported by survivability and ground-casualty assessments.
    6. Failure scenarios must be modelled and shared: Operators have to analyse and share failure scenarios, fragmentation patterns, ballistic coefficients, de-orbit plans, flight-path angles and danger zones.
    7. Surviving and hazardous components must be identified: They must identify components likely to survive re-entry, and hazardous systems such as batteries and pressure vessels.
    8. A number makes sustainability measurable: By requiring quantitative studies and attaching a figure to the acceptable risk threshold, the guidelines make sustainability measurable and therefore trackable.
    9. Permissions: IN-SPACe will re-verify the latest re-entry parameters approximately three months before the proposed operation.
    10. A post-launch decision needs six months’ notice: If a planned re-entry is decided upon after launch, the operator must apply at least six months in advance.
    11. Airspace and maritime warnings at 45 days: Operators must obtain an IN-SPACe advisory note to issue warnings to airborne and marine vessels in the re-entry area at least 45 days before the re-entry begins.
    12. A foreign jurisdiction requires that state’s clearance: If a re-entry site falls within the territorial control of a non-Indian state, including its exclusive economic zone, the applicant must submit the relevant clearance or authorisation from that state.
    13. The checkpoints are intervention windows: These checkpoints give the regulator fixed windows and mechanisms to intervene when re-entry parameters change after the mission has launched, or when the risk pattern changes.

    What international framework do the guidelines translate?

    1. The development period: For nearly two decades the international community has developed principles for sustainable space activities.
    2. The two leading instruments: They are the Inter-Agency Space Debris Coordination Committee’s Space Debris Mitigation Guidelines, and the Guidelines for the Long-term Sustainability of Outer Space Activities of the United Nations Committee for the Peaceful Uses of Outer Space.
    3. The treaty foundation: Article IX of the Outer Space Treaty 1967 provides an important foundation for environmental responsibility in the conduct of space activities.
    4. The working definition of sustainability: The UN Guidelines define sustainability as maintaining space activities while preserving the outer space environment for future generations.
    5. The structural weakness of that architecture: Most of the contemporary sustainability architecture works on guidelines and other similar forms of soft law, which operators are not obligated to follow.
    6. How the national regulator closes it: The IN-SPACe guidelines solve this problem for India by tying an operator’s fragmentation analysis and insurance policies to the national regulator, which converts a voluntary standard into a condition of permission.

    How do the guidelines handle liability?

    1. The treaty position on liability: The Space Liability Convention 1972 places absolute liability on a launching state for damage caused by its space object on the surface of the earth, or to aircraft in flight.
    2. The state carries the claim, not the operator: Absolute liability means the launching state answers for the damage regardless of fault, so a private failure becomes a sovereign liability by default.
    3. The guidelines invert that internally: Operators must undertake planned re-entries at their own risk, and they remain liable for third-party damage and claims.
    4. Indemnity to the government: Operators indemnify the Government of India and its agencies for liability incurred under India’s international commitments.
    5. Insurance as the backing: Operators must satisfy the applicable third-party insurance requirements, so the indemnity is funded rather than merely promised.

    Challenges to the IN-SPACe planned re-entry guidelines

    1. The regulator has no statutory backing: IN-SPACe functions as the sector’s regulator without legislative authority, so its guidelines rest on executive policy rather than on an Act. Eg. India has no dedicated space activities legislation, and the Indian Space Policy 2023 is a policy document. Fix. Enact a space activities law placing authorisation, liability and penalties on a statutory footing.
    2. The regulator sits inside the body it regulates: IN-SPACe authorises activities of private companies and government entities including ISRO, and it operates under the Department of Space. Eg. The same department is both the policy custodian and the parent of the entity it must clear. Fix. Place IN-SPACe under an independent appointments and reporting structure, with appeals lying outside the Department of Space.
    3. No appellate route for a refused authorisation: An operator refused authorisation, or held to a risk finding it disputes, has no defined appeal forum. Eg. The guidelines fix a casualty risk threshold without naming any forum before which an operator may contest a risk finding. Fix. Constitute a space disputes appellate tribunal with technical members, on the model used for telecom and electricity regulation.
    4. Verification capacity lags the requirement: A casualty risk below 1 in 10,000 must be independently verifiable, and that requires tracking and modelling capability the regulator does not itself hold. Eg. Debris tracking rests on ISRO’s Project NETRA, which is oriented to collision avoidance rather than to re-entry survivability audit. Fix. Build an independent re-entry analysis cell with access to radar and optical tracking data, empanelling accredited third-party assessors.
    5. Insurance capacity is untested at Indian scale: Third-party space insurance is a thin market, and a small operator may be unable to price cover for a low-probability, high-consequence event. Eg. Indian space startups have grown from a handful to around 200, most of them without balance sheets that carry catastrophic risk. Fix. Create a graded liability cap with a government-backed pool above it, on the model used for civil nuclear liability.

    “[2026] Consider the following statements about involvement of private entities in India’s space programme:

    1. IN-SPACe is an autonomous agency formed to facilitate participation of private entities.

    2. Agnikul Cosmos launched the world’s first flight using 3D-printed rocket engine.

    3. Skyroot Aerospace has developed liquid fuel for GSLV.

    (a) 1 only

    (b) 2 and 3 only

    (c) 1 and 2 only

    (d) 1, 2 and 3

  • After Minister, Secy’s kin availed of agri subsidy scheme, new rules bar them

    After Minister, Secy’s kin availed of agri subsidy scheme, new rules bar them

    Why in the News

    The National Horticulture Board (NHB) amended the Scheme Guidelines of the Commercial Horticulture and Cold Storage Schemes on 21 August 2026, with immediate effect. The amendment bars holders of constitutional posts, serving ministers, members of legislatures, mayors, district panchayat chiefs and government employees from financial assistance under NHB schemes, and redefines ‘family’ to cover the applicant’s spouse, father, mother, sons and daughters. It follows a 27 June 2026 investigation reporting that a Union Minister of State and the kin of a serving Central government Secretary had availed subsidy for their cucumber farms. The tension is that a scheme designed to promote large scale commercial horticulture had eligibility rules loose enough to route public subsidy to the families of the officials administering the sector.

    What is the Development of Commercial Horticulture scheme?

    1. Purpose: The scheme, formally the Development of Commercial Horticulture through Production and Post-Harvest Management of Horticulture Crops, promotes commercial farming of horticultural crops on a large scale, meaning cultivation for profit rather than subsistence.
    2. Crops covered: It covers three vegetables, capsicum, cucumber and tomato, and eight varieties of flowers including rose, lilium and chrysanthemum.
    3. The assistance it offered: The scheme offered a maximum subsidy of 50 per cent of the project cost, capped per family.
    4. Who runs it: It is administered by the National Horticulture Board, an autonomous body under the Ministry of Agriculture and Farmers’ Welfare.

    Who is now barred from the subsidy?

    1. Constitutional post holders: Present holders of constitutional posts are ineligible for financial assistance under NHB schemes.
    2. Elected representatives and office bearers: Present ministers and ministers of state, members of the Lok Sabha and the Rajya Sabha, members of State Legislative Assemblies and Councils, mayors of municipal corporations and chairpersons of district panchayats are ineligible.
    3. Serving government employees: Serving employees of Central and State government ministries and departments, public sector undertakings, autonomous bodies and local bodies are ineligible, except Multi-Tasking Staff, Class-IV and Group D employees.
    4. Pensioners above a threshold: Superannuated and retired pensioners receiving a monthly pension of Rs 10,000 or more are ineligible, excluding the same Multi-Tasking Staff, Class-IV and Group D categories.
    5. Groups of farmers: A group of farmers is the fifth barred category, closing the route by which several individuals could apply jointly.
    6. A single concession: Family members of persons in the barred categories may avail one-time assistance, subject to the revised definition of family.

    How has the definition of ‘family’ changed?

    1. The new definition: For determining eligibility under NHB schemes, ‘family’ now comprises the applicant’s spouse, father, mother, sons and daughters.
    2. The definition it replaces: The old guidelines defined family as the husband, wife and dependent minor children, which left adult children and parents free to apply separately.
    3. One member per family: Only one member of a family is eligible to avail financial assistance under NHB schemes, whether individually or through a Hindu Undivided Family, a partnership or proprietorship firm, or as a director of a company.
    4. Assistance is attributed to the family: Financial assistance availed by any member of a family is treated as assistance availed by that family, and no further assistance is admissible to any other member under any NHB scheme or component.
    5. The unutilised balance is forfeited: The bar applies irrespective of any unutilised portion of the maximum admissible ceiling, and constitutes the final entitlement of the family across all NHB schemes and components.

    What else did the amendment change?

    1. The subsidy rate was cut: The subsidy component was reduced from 50 per cent to 35 per cent for beneficiaries in general category states.
    2. A higher rate for hill and North Eastern states: The rate is 45 per cent in North Eastern and Himalayan states, retaining a differential for higher cost regions.
    3. Cold storage assistance was capped: The maximum subsidy for cold storage capacity was capped at Rs 2 crore.
    4. A voluntary exit route was created: A beneficiary may, during the prescribed lock-in period, voluntarily opt out by refunding the entire subsidy amount with applicable interest, and is then discharged from the obligations and restrictions arising from the assistance.
    5. Misrepresentation now carries recovery: Suppression, misrepresentation or furnishing of incorrect information to obtain assistance renders the applicant liable for recovery of the assistance released, along with applicable interest.
    6. The stated objective: The NHB circular states the amendments are meant to rationalise financial assistance, ensure equitable distribution of benefits, prevent duplication of subsidy, and make implementation more transparent and effective.

    What prompted the amendment?

    1. The Minister’s own case: A 27 June 2026 report found that Bhagirath Choudhary, Minister of State in the Union Ministry of Agriculture and Farmers’ Welfare, availed a Rs 99 lakh subsidy for his farm in 2025 under the same scheme administered by his own ministry.
    2. The subsidy was returned: He returned the subsidy amount to the government a month later.
    3. The Secretary’s kin: The same investigation showed that the wife, son and mother of senior Indian Administrative Service officer Naresh Pal Gangwar, then serving as Secretary of the Department of Animal Husbandry and Dairying, were among the beneficiaries of the scheme.
    4. A posting was withdrawn: The government appointed that officer as Higher Education Secretary on 23 July 2026, and cancelled the appointment on 10 August 2026 before he joined.
    5. The design gap the cases exposed: Neither case required a false declaration, because the old ‘family’ definition covered only husband, wife and dependent minor children, and no category of applicant was excluded by office.

    Challenges to the National Horticulture Board subsidy scheme

    1. Verification of family relationships is self declared: The Board has no independent database linking an applicant to parents, adult children or spouse, so the widened definition depends on the applicant disclosing it. Eg. The barred cases surfaced through a newspaper investigation rather than through scheme level scrutiny. Fix. Seed applications with Aadhaar based family linkage from the ration card or land record database, so a second application from the same family is flagged automatically.
    2. Corporate structures can defeat the one-member rule: The bar covers a Hindu Undivided Family, a firm and a directorship, but not shareholding through nominees or layered entities. Eg. The revised rule lists specific vehicles rather than applying a beneficial ownership test. Fix. Apply a beneficial ownership disclosure requirement above a defined shareholding threshold, on the model used for company law filings.
    3. A lower subsidy rate deters the small grower: The reduced rate raises the own contribution needed for a poly-house or a cold store, which is harder for a one hectare holder than for a large operator. Eg. Protected cultivation and cold storage carry high fixed setup costs regardless of holding size. Fix. Retain the higher rate for small and marginal holders and Farmer Producer Organisations while applying the reduced rate to larger project sizes.
    4. Cold storage assistance concentrates geographically: Capital subsidy flows to states that already have storage clusters and applicants able to raise the balance capital. Eg. Cold storage capacity in India remains concentrated in a few states, leaving wide gaps elsewhere. Fix. Ring-fence a share of the cold storage corpus for districts with no existing capacity, appraised against a mapped storage deficit.
    5. Lock-in monitoring is weak: The new voluntary exit and recovery provisions assume the Board can track asset use through the lock-in period, which requires physical inspection capacity it does not have. Eg. The guidelines rely on the beneficiary approaching the Board rather than on periodic verification. Fix. Mandate geo-tagged and time-stamped asset verification at fixed intervals during the lock-in, released through the scheme portal.
    6. No public beneficiary register exists: Without a searchable list of who received what, the same defect can recur undetected until it is reported externally. Eg. Both the Minister’s case and the Secretary’s family’s case came to light through an outside investigation. Fix. Publish a district-wise beneficiary register with name, project and sanctioned amount, on the model of the public disclosure already used for fertiliser and food subsidy transfers.

    Conclusion

    The scheme guidelines have been amended by an NHB circular dated 21 August 2026 and apply with immediate effect, so the barred categories and the widened family definition already govern fresh applications. The amendment also cuts the subsidy rate for general category states, caps cold storage assistance at Rs 2 crore, and creates a voluntary refund route out of the scheme. The circular sets no further date or review milestone, and the operative test will be whether the widened family definition is verified at application stage rather than after the fact.

    “[2018, GS3, 15 marks] Assess the role of National Horticulture Mission (NHM) in boosting the production, productivity and income of horticulture farms. How far has it succeeded in increasing the income of farmers?”

  • SC committee pulls up Assam government for inaction over mining activity near Kaziranga

    SC committee pulls up Assam government for inaction over mining activity near Kaziranga

    Why in the News

    The Central Empowered Committee (CEC), a body constituted by the Supreme Court to monitor compliance with its forest and wildlife orders, has held that the Assam government did not address mining in and around the Parkup Pahar Range with the seriousness it deserved. The Range is a declared wildlife sanctuary and a vital ecological corridor on the southern boundary of Kaziranga National Park. The finding follows the CEC’s own directions of 30 May 2025 to the Assam Chief Secretary, which required a comprehensive Watershed Drainage Analysis Report and quarterly status reports on action against reported violations. The Supreme Court had already restrained all mining along Kaziranga’s southern boundary by an order of 12 April 2019. The committee’s finding lands amid a separate controversy over a plan to reduce the Eco-Sensitive Zone of Kaziranga National Park, so the State is seeking a narrower buffer while existing court-ordered protections remain unimplemented.

    What is the Central Empowered Committee?

    1. A court appointed compliance body: The CEC was constituted by the Supreme Court to monitor and report on compliance with its orders in forest and wildlife matters, and to examine applications referred to it.
    2. What it produces: It examines complaints and applications, calls for records from State governments, and files reports and recommendations to the Supreme Court on which the Court then acts.
    3. How its directions reach a State: It writes directly to the State Chief Secretary with timelines and required submissions, and treats the absence of a submission as a compliance failure to be reported.

    What did the Right to Information trail reveal?

    1. The application: A Right to Information (RTI) application was filed on 15 June by an environmental activist, seeking details of compliance with those directions.
    2. What was sought: It asked for copies of both submissions the directions had required of the State.
    3. The core document is missing: The CEC’s response of 17 July stated that it did not receive the comprehensive Watershed Drainage Analysis Report.
    4. Partial compliance only: The response said the quarterly status reports had been attached, and it does not show that the watershed exercise the CEC ordered was completed.
    5. The deadline had already passed: The CEC had fixed October 2025 as the deadline for submitting the watershed report.

    What does the earlier record show?

    1. The originating proceeding: The CEC recorded allegations of renewed mining in Parkup Pahar in the same report that carried those directions, filed on “Application No.1592 of 2024”.
    2. The standing court order: The Supreme Court’s order of 12 April 2019 restrained all mining and related activities along the southern boundary of Kaziranga, and throughout the catchments of rivers and streams originating in the Karbi Anglong hills and flowing into the park.
    3. The construction bar: The same order prohibited new construction on private lands forming part of nine identified animal corridors.
    4. Mining continued regardless: On 3 March the CEC wrote to the Assam Chief Secretary noting that mining and related activities had been going on “in some way on one pretext or the other”.
    5. The local authority’s conduct: The same letter recorded that the Karbi Anglong Autonomous Council (KAAC) was not presenting a holistic picture to the courts. The KAAC administers Karbi Anglong, the district that forms Kaziranga’s southern boundary.
    6. A six year old request remains unanswered: The CEC’s letter of 6 May 2021 specifically asked the State government to communicate the action taken to notify the animal corridors connecting the park, and no response was received. The catchment areas of the streams and rivers originating in the Karbi Anglong hills remain unidentified after more than six years.

    Why does the elephant reserve finding matter?

    1. The area is doubly protected: The Parkup Pahar Range is both a declared wildlife sanctuary and a vital ecological corridor linking the park to the Karbi Anglong hills.
    2. The reserve covers both landscapes: The Karbi Anglong Elephant Reserve covers Karbi Anglong and Kaziranga National Park, so the mining sits inside a notified elephant reserve rather than beside one.
    3. Documented damage: The panel pointed to areas of the reserve ravaged by large-scale mining, and to the death of several elephants due to conflicts with humans.
    4. The leases themselves are questioned: The CEC asked how mining leases around Borjuri could have been sanctioned at all, given that the area forms part of the elephant reserve.
    5. The administering council is faulted directly: The CEC recorded that the KAAC “seems oblivious of all these notifications and is not conscious of the measures that need to be taken to protect the ecology and wildlife of the area”.
    6. The corridor system is the stake: Animal corridors are what allow a population to move between the park and the hills, so an unnotified corridor is legally open to the construction the 2019 order sought to bar.

    Challenges to enforcing the Central Empowered Committee’s directions

    1. The committee has no independent enforcement power: The CEC reports and recommends, and only the Supreme Court can compel a State, so a State that misses a deadline faces no immediate consequence. Eg. The October 2025 watershed report deadline passed without the report and without penalty. Fix. Attach a default consequence to a missed CEC deadline, such as automatic suspension of fresh mineral concessions in the area concerned until the submission is filed.
    2. Autonomous councils sit outside the reporting chain: Sixth Schedule councils administer land and minor minerals in their areas, and directions addressed to the State Chief Secretary do not bind them directly. Eg. The National Green Tribunal’s 2014 ban on rat-hole coal mining in Meghalaya was directed at the State government, while the land it covered is administered by Sixth Schedule district councils. Fix. Make the autonomous council a named respondent in compliance proceedings covering its area, with its own filing obligation.
    3. Corridor notification is discretionary in practice: Corridors are identified in reports and remain unnotified, so no legal restriction attaches to the land inside them. Eg. Nine animal corridors around Kaziranga identified in 2019 remain unnotified. Fix. Set a statutory deadline after identification, on the expiry of which the corridor stands provisionally notified pending State action.
    4. Baseline studies are the first casualty of delay: Watershed and catchment mapping is expensive and slow, and its absence makes every subsequent violation hard to establish. Eg. The catchment areas of streams flowing into the park remain unidentified after more than six years. Fix. Fund catchment mapping from the Compensatory Afforestation Fund and commission it through a central technical agency rather than the defaulting State.
    5. Compliance is monitored through citizen requests: The gap in this case surfaced through a private RTI application rather than through a compliance dashboard. Eg. The missing watershed report was revealed by an activist’s application of 15 June. Fix. Publish CEC directions and their compliance status on a public portal, so a lapsed deadline is visible without an application.

    Conclusion

    The Central Empowered Committee has reiterated that its earlier recommendations remain unimplemented and has asked the Assam government to ensure speedy implementation of all of them, and to immediately notify the nine identified animal corridors. The current status is that the watershed report is outstanding, the corridors are unnotified, and mining leases inside the Karbi Anglong Elephant Reserve remain unexplained. The next milestone is the State’s response on the corridor notification and the watershed exercise, alongside the separate decision on the proposed reduction of Kaziranga’s Eco-Sensitive Zone. The case turns on compliance rather than on the adequacy of the law, since the restraining order and the protected area notifications already exist.

    “[2026] With reference to Madhav National Park, which of the following statements is/are correct?

    1. It was declared a Tiger Reserve in India in 2025.

    2. Sakhya Sagar, which is designated as a Ramsar Site, is situated within this National Park.

    3. Its area is shared between Madhya Pradesh and Rajasthan.

    (a) 1 only

    (b) 1 and 2

    (c) 2 and 3

    (d) 3 only

  • Barren-land fallacy

    Why in the News

    The barren-land fallacy is the assumption that land without tree cover is barren land, therefore ecologically deficient, and that it will evolve or should be helped to evolve into a forest. Tree-planting drives in India often perpetuate this fallacy by planting trees in the wrong ecosystems, and by planting species unsuited to local conditions. Trees deliver cooling, soil retention, carbon storage and habitat only where they belong ecologically, and outside that setting they cause harm. Ecological restoration therefore does not always mean planting more trees, and the assumption that it does drives programmes that damage the ecosystems they claim to repair.

    What is the barren-land fallacy?

    1. The assumption in full: Land that does not carry tree cover is treated as barren, and barren land is in turn treated as ecologically deficient.
    2. The second step in the chain: Such land is then assumed to evolve, or to require help to evolve, into a forest, so absence of trees is read as an incomplete stage rather than a stable state.
    3. Where it becomes policy: The fallacy enters practice through tree-planting drives that select land for planting on the basis of missing tree cover rather than on the basis of what ecosystem the site naturally supports.

    What do trees actually do, and when do those benefits hold?

    1. Local cooling: Trees lower air and surface temperature in the neighbourhood in which they stand, through shade and through the water they release into the air.
    2. Soil retention: Root systems hold soil in place and slow the runoff that strips it, which is why tree cover reduces erosion on slopes and along stream banks.
    3. Carbon drawdown and storage: Trees take carbon dioxide from the air and lock the carbon into wood and soil, which is the basis of the climate value claimed for planting.
    4. Habitat provision: A stand of trees supports birds, small animals and insects that depend on canopy, bark and leaf litter for nesting and for food.
    5. The benefits are conditional, not automatic: Each of these effects holds only where a forest is the ecosystem the site naturally supports. Planted outside it, the same trees do damage.

    How do India’s tree-planting drives reproduce the fallacy?

    1. Planting in the wrong ecosystem: Drives place trees on land where a forest is not the native ecosystem, so the planting displaces the system that belongs there rather than restoring one.
    2. Species unsuited to local conditions: Drives often plant trees unsuitable for the site, such as eucalyptus, which draws a great deal of water, in a water-stressed area.
    3. Offsets substitute for restoration: Compensatory afforestation is treated as an equivalent to the forest cleared, and plantations cannot replace natural forests in biodiversity terms.
    4. Counting favours planting over ecosystem type: Programme performance is measured in area planted and saplings established, which gives no credit for protecting a grassland or a scrubland in place.
    5. Invasive species compound the damage: Species introduced to green open land spread beyond the planting site and suppress native ground cover. Prosopis juliflora has spread across Rajasthan, degrading grasslands.

    What counts as ecological restoration instead?

    1. The correct objective: To ecologically restore a place does not always mean planting more trees. It is to do whatever will protect or restore the ecosystem that would naturally occur there.
    2. When planting is the right answer: Planting trees or regenerating forests is valuable where forests have been degraded or cleared, and where a forest is the native ecosystem.
    3. Forests are one ecosystem among several: Others include grasslands, savannah, scrubland, wetlands and deserts, along with many naturally open ecosystems.
    4. The dryland assumption has a documented cost: A University of California professor of history and geography has written that the assumption that the world’s drylands are worthless, deforested and overgrazed landscapes has led to programmes and policies that have often systematically damaged dryland environments.
    5. The semi-desert is not a failed forest: The Sahel region in Mali is a semi-desert landscape, and treating such a landscape as degraded forest misidentifies both its baseline and its restoration target.

    Challenges to restoring India’s open natural ecosystems

    1. Open ecosystems have no protective legal category: Grasslands, savannahs and scrublands are classified as wasteland or revenue land in official records, so they can be allotted for planting or development without a diversion clearance. Eg. India’s grassland area has declined 31 per cent, from 18 million hectares to 12.3 million hectares. Fix. Create a distinct notified category for open natural ecosystems in land records, with diversion requiring the same clearance a forest diversion needs.
    2. Restoration targets are stated in tree cover: National and international commitments are measured in hectares brought under tree and forest cover, which makes planting the only countable action. Eg. India’s Bonn Challenge pledge is stated as restoring 26 million hectares by 2030. Fix. Report restoration by ecosystem type against a mapped reference state, so a restored grassland counts as much as a planted hectare.
    3. Species selection ignores water budgets: Fast growing exotics are chosen for survival rates rather than for their draw on local groundwater. Eg. Eucalyptus plantations in water-stressed districts lower the water table they depend on. Fix. Make a site water balance assessment a precondition to species approval in any planting programme.
    4. Open ecosystem species lose habitat to greening: Ground nesting and open country species need the absence of tall vegetation, so planting removes their habitat directly. Eg. Grassland conversion has driven the decline of the Great Indian Bustard and the Lesser Florican. Fix. Map and exclude critical open country species habitat from all afforestation and green cover programmes.
    5. Baseline ecosystem maps do not exist at working scale: Without a map of what ecosystem a site naturally supports, the planting decision defaults to the presence or absence of trees. Eg. Desertification assessment records 105.48 million hectares as degraded without separating naturally open land from degraded forest. Fix. Publish a national reference ecosystem map at the level of the revenue village, and tie every restoration sanction to it.

    Conclusion

    The barren-land fallacy treats absence of tree cover as a deficiency to be corrected, and the correction damages ecosystems that were never forests. Restoration means returning a site to the ecosystem that would naturally occur there, which in a grassland, a scrubland or a desert means protecting openness rather than closing it with canopy. The immediate consequence is that a planting drive on the wrong site is a conversion, not a restoration. Whether India’s restoration accounting can measure ecosystem type rather than tree cover is what determines if the fallacy continues to be funded.

    Matching Previous Year Question

    “[2015] Which one of the following is the best description of the term ‘ecosystem’? (a) A community of organisms interacting with one another (b) That part of the Earth which is inhabited by living organisms (c) A community of organisms together with the environment in which they live (d) The flora and fauna of a geographical area ANSWER: (c)”

  • Importance of elephant corridors in reducing conflict

    Why in the News

    A Supreme Court Bench led by the Chief Justice of India directed the Centre to conduct a fresh survey of elephant corridors. The Bench held that these corridors cannot be blocked out of a fear of crop damage, because elephant herds by nature travel long distances. The Bench was hearing a writ petition on managing human-elephant conflict, which had earlier sought to prevent the use of fireballs, spikes and similar materials to drive elephants away. In its order the Bench asked the Centre to indicate the steps taken to prohibit those methods and any other “coercive measure” used to divert the natural movement of elephants. The direction sets a conservation requirement against the immediate economic loss of farmers whose fields lie along those routes.

    What is an elephant corridor?

    1. Definition: An elephant corridor is a movement pathway connecting two natural habitats, allowing herds to pass between them without entering settled land.
    2. The mapped stock: The government’s last major mapping exercise, in 2023, documented 150 elephant corridors across 15 states.
    3. Why they carry legal weight: Disrupting a corridor both sparks human-elephant conflict and poses a direct threat to the animals, which is why blockage is treated as a conservation failure rather than a land use choice.

    Why do elephants need to move over long distances?

    1. They are highly mobile mammals: Asian elephants are highly mobile and social animals, and a male elephant’s average home range, the area it typically uses for food, water and shelter, extends between 50 and 300 sq km.
    2. Movement takes more than one form: Elephants move through their range in herds or individually, so a corridor must accommodate both group and solitary passage.
    3. Home ranges are not fixed: Ranges extend or contract depending on habitat type, food availability, water sources, population density and human disturbance.
    4. Movement is seasonal: Elephant movement is strongly influenced by the seasonal and spatial distribution of food, water and habitat.
    5. A documented seasonal pattern: Seasonal migrations into Kerala are common during the dry months, as elephants seek water and food in the relatively moist forests of the Western Ghats, according to a recent Environment Ministry report.
    6. Climate has driven movement historically: Climate change and drought have both played a role in forcing elephant migration.

    What does an intact corridor actually deliver?

    1. Genetic exchange: Corridors allow elephants genetic exchange or dispersal, which maintains diversity in their population pool and prevents isolated herds from inbreeding.
    2. Seasonal resource access: Well conserved corridors give elephants access to food and water in natural habitats during seasonal changes.
    3. Fewer crop encounters: That access is what prevents elephants entering crop lands, so the corridor is the mechanism that reduces the crop damage farmers fear.
    4. Reduced mortality: A functioning pathway removes the need for herds to cross highways, railway lines and settlements, which is where non natural elephant deaths occur.

    What is fragmenting the corridors?

    1. Infrastructure construction: The major issues in recent disruptions are infrastructure construction and industrial and mining activity along or across corridor routes.
    2. Linear infrastructure specifically: Highways, existing and new railway lines, canals and power lines push elephants towards farmlands and human settlements, triggering conflict.
    3. Land use change on the fringes: Changing land use, plantations and farmland fencing fragment habitat at its edges, which closes off the approaches to a corridor even where the corridor itself survives.
    4. Mining pressure drives displacement: Habitat loss and mining pressure have pushed elephants out of established ranges and into new ones over the past two decades.

    Where is the fragmentation worst?

    1. India has four principal elephant landscapes: These are the Western Ghats; the North-Eastern Hills and Brahmaputra floodplains; the Shivalik Hills and Gangetic plains; and Central India and the Eastern Ghats.
    2. Western Ghats: The habitat of the Western Ghats population is rapidly fragmenting owing to changing land use, plantations and farmland fencing, as noted in the last all-India elephant population estimation report released in 2025.
    3. Shivalik and Brahmaputra: The same report recorded similar disruptions in the Shivalik and Brahmaputra plains.
    4. Central India and Eastern Ghats: This landscape carries the biggest challenge, with Chhattisgarh receiving elephants arriving from Jharkhand and Odisha over two decades owing to habitat loss and mining pressures.
    5. The range is still expanding: Elephants have also expanded their range into parts of Madhya Pradesh and Maharashtra, creating conflict in states with no history of managing elephant populations.

    What has the Court asked the Centre to do?

    1. A fresh survey: The Centre must conduct a fresh survey of elephant corridors, which resets the 2023 mapping exercise as the operative baseline.
    2. Crop damage is not a ground for blockage: Corridors cannot be blocked out of a fear of crop damage, since elephant herds by nature travel long distances.
    3. An account of prohibition steps: The Centre must indicate the steps taken to prohibit the use of fireballs, spikes and similar materials to drive elephants away.
    4. A wider category of prohibited action: The order extends to any other “coercive measure” used to divert the natural movement of elephants, which covers methods the petition did not name.

    Challenges to protecting elephant corridors

    1. Corridors have no independent legal status: A mapped corridor is not a notified protected area, so land inside it can be diverted for a project without triggering the safeguards that apply to a sanctuary. Eg. The 150 corridors mapped in 2023 span forest, revenue and private land with differing tenure rules. Fix. Notify identified corridors as conservation reserves or ecologically sensitive areas so diversion requires the same clearance as protected area land.
    2. The mapping baseline is dated and voluntary: The last major mapping was completed in 2023 and carries no requirement that project appraisals check against it. Eg. The Court had to direct a fresh survey rather than rely on a periodic statutory exercise. Fix. Fix a statutory five year corridor survey cycle and make the corridor layer a mandatory input to environmental clearance appraisals.
    3. Linear projects are cleared one at a time: Each highway, railway line, canal and power line is assessed on its own merits, so the cumulative severance of a corridor never appears in any single appraisal. Eg. Elephant movement in Central India was reshaped by two decades of accumulated mining and infrastructure pressure rather than any one project. Fix. Mandate landscape level cumulative impact assessment for all linear infrastructure crossing a mapped corridor.
    4. Mitigation structures are built to the wrong specification: Underpasses and overpasses are often sized for smaller species and sited for engineering convenience rather than on observed elephant crossing points. Eg. Corridors carry herds as well as solitary bulls, whose passage needs differ. Fix. Tie animal passage design to radio collar and camera trap movement data for that specific corridor before construction is approved.
    5. Corridors cross state boundaries with no joint manager: Elephants moving between Jharkhand, Odisha and Chhattisgarh pass through three forest administrations with separate budgets and separate priorities. Eg. Chhattisgarh has absorbed elephants displaced from two neighbouring states over two decades. Fix. Constitute statutory inter-state elephant landscape authorities with a pooled budget and a single management plan for each of the four landscapes.
    6. Farmers carry the cost of a conservation decision: A ruling that a corridor cannot be blocked leaves the crop losses on the cultivator who farms beside it. Eg. Seasonal migration into Kerala during the dry months moves herds through cultivated valleys. Fix. Link corridor notification to guaranteed, time bound crop compensation through direct benefit transfer, so protection and compensation are notified together.

    Conclusion

    The Court has established that an elephant corridor is not negotiable against crop protection. The binding constraint is that the network was last mapped in 2023, while infrastructure, mining and land use change have continued to cut across it in all four elephant landscapes. The next step is the Centre’s response to the two directions recorded above, on the fresh survey and on prohibition.

    Back2Basics: The Asian Elephant

    1. Status: The Asian elephant is listed as Endangered on the IUCN Red List and is placed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES).
    2. Domestic protection: It is listed in Schedule I of the Wildlife (Protection) Act, 1972, the highest level of protection available under Indian law.
    3. Range: India holds the largest wild population of the species, distributed across the four elephant landscapes covering the Western Ghats, the North East, the Shivaliks and Central India with the Eastern Ghats.

    Matching Previous Year Question

    “[2022] With reference to Indian laws about wildlife protection, consider the following statements : 1. Wild animals are the sole property of the government. 2. When a wild animal is declared protected, such animal is entitled for equal protection whether it is found in protected areas or outside. 3. Apprehension of a protected wild animal becoming a danger to human life is sufficient ground for its capture or killing. Which of the statements given above is/are correct ? (a) 1 and 2 (b) 2 only (c) 1 and 3 (d) 3 only ANSWER: (a)”

  • AI is transforming cyber attacks as well as defences: What this means for India

    Why in the News

    Artificial Intelligence (AI) is now amplifying cyber threats across the cyber kill chain at speed, scale and sophistication, and is developing the ability to act as an autonomous agent that identifies, plans, adapts and carries out offensive cyber operations. The shift follows the fastest technology adoption on record: the Internet took 15 years to reach a billion users, and ChatGPT did so in three. The tension is that AI capability is concentrated in very few countries, so the same technology that raises the threat also determines who can defend against it. India’s indigenous AI ecosystem lags the United States and China across the entire AI stack, leaving it exposed on both sides of that equation.

    What is the cyber kill chain?

    1. Definition: The cyber kill chain is the sequence of stages an attacker must complete to succeed, running from reconnaissance on a target, through weaponisation of malicious code, to command and control of the compromised system.
    2. Why the framework matters: Defence has traditionally worked by breaking any one link in that chain, since an attack that fails at one stage cannot proceed to the next.
    3. What AI changes: AI is now compressing or automating several stages at once, so breaking a single link no longer stops the sequence.

    How is AI amplifying offensive cyber operations?

    1. Reconnaissance is automated: Gathering information about a target once depended on humans, and research shows ChatGPT models being used to mine social media for precise details to craft AI generated phishing emails.
    2. Deepfakes are now real time: AI is generating real time deepfakes, deepening confusion about what is authentic online.
    3. Social engineering scales: AI enabled social engineering, the use of AI to trick or persuade people into taking harmful actions, no longer requires a human operator per target.
    4. Malware no longer holds a fixed shape: Large language models (LLMs) can autonomously generate, modify and restructure polymorphic malware to suit the situation, unlike traditional malware, which relies on fixed signatures and predictable patterns.
    5. An AI has already run an attack chain: In September 2025 Anthropic claimed a Chinese state sponsored group, GTG-1002, had used Claude Code as an autonomous cyber agent across multiple stages of an attack, in what the company called the first reported case of an AI orchestrated cyber espionage campaign.

    Why does autonomous vulnerability discovery change the risk?

    1. Zero-days are being found at scale: Anthropic’s latest frontier model, Claude Mythos Preview, has identified thousands of zero-day vulnerabilities, meaning flaws previously unknown to developers, across major operating systems and browsers, many of them critical.
    2. It builds the exploits too: The model developed related exploits largely without human intervention, collapsing the gap between finding a flaw and being able to use it.
    3. Hardened systems are not exempt: It found a 27 year old vulnerability in OpenBSD, an operating system reputed to be highly security hardened and widely used to run firewalls and critical infrastructure.
    4. Industrial systems are the exposed surface: Such vulnerabilities are especially dangerous for Operational Technology (OT) and Industrial Control Systems (ICS), the computing that governs nuclear facilities, energy grids, pharmaceutical manufacturing, chemical processing, oil refineries and communication networks.
    5. Exposure grows with integration: That infrastructure becomes more exposed as it integrates further with AI, so the adoption that improves efficiency also widens the attack surface.

    Why do old cyber defences no longer hold?

    1. Signature matching fails against shape shifting code: Traditional antivirus looks for known malware fingerprints, which malware that constantly changes and adapts no longer presents.
    2. Static patching is too slow: Security patches written for known vulnerabilities are far less effective when new flaws are discovered and weaponised faster than patch cycles run.
    3. AI defence works differently: AI in cybersecurity enables real time threat detection, automated response and large scale data analysis, mitigating risks faster than human led triage.
    4. The divide has shifted: The real AI divide is not about who uses AI but about who builds it and who controls its development, which is why cybersecurity capability now tracks AI capability.

    How exposed is India?

    1. A nuclear plant’s data was posted: The ransomware group World Leaks claimed to have stolen and posted data related to India’s largest nuclear plant, Kudankulam, including blueprints of facility parts and supplier details.
    2. The ranking moved sharply: Cyber intelligence firm CloudSEK’s 2024 report placed India as the second most cyber attacked nation after the United States, and its 2025 report placed India sixth.
    3. State backed actors targeted defence during a conflict: During Operation Sindoor, Pakistan backed threat actors such as APT36 targeted India’s critical sectors, including the Ministry of Defence, the Army, the Navy and the Defence Research and Development Organisation (DRDO).
    4. A new target class appeared: The same campaign targeted Bharat Operating System Solutions (BOSS) Linux for the first time, extending the attack surface to India’s indigenous operating system.

    Can India defend a cyberspace built on an AI stack it does not own?

    1. The ecosystem is incremental: India’s indigenous AI ecosystem remains incremental and lags well behind the United States and China across the AI stack.
    2. The gap is at every layer: The shortfall runs across foundational models, graphics processing units, chip design and large scale data centre infrastructure, so no single procurement closes it.
    3. Dependence is the security problem: The lag leaves India heavily dependent on the United States and other technologically advanced countries for the very tools its defence now requires.
    4. Capability determines both roles: Countries with leading AI ecosystems gain a greater ability both to conduct sophisticated cyber campaigns and to defend against them, so dependence caps India’s ceiling on defence as well as deterrence.

    What has India done so far?

    1. CERT-In has shifted its methods: The Indian Computer Emergency Response Team (CERT-In), the national agency for responding to cyber security incidents, has since 2025 adopted AI driven threat detection, cyber resilience measures, trusted AI frameworks and citizen centric malware mitigation.
    2. A specific advisory was issued: In April 2026 it issued an advisory for organisations on defending against AI driven cyber risks.
    3. The advisory’s operative instructions: Recommendations included “removing unnecessary internet-facing services” and treating every newly discovered vulnerability as something that “could be exploited within hours, not weeks”.
    4. Governance work is at the framework stage: The Ministry of Electronics and Information Technology (MeitY) is exploring a consent based framework for synthetically generated content, alongside curbs on agentic AI autonomy and clearer liability frameworks for AI models.

    Challenges to India’s AI-enabled cyber defence

    1. Defence rests on advisories rather than obligations: CERT-In’s guidance to organisations is recommendatory, so a private operator of critical infrastructure faces no penalty for ignoring it. Eg. The April 2026 advisory asked organisations to remove unnecessary internet facing services, with no compliance audit attached. Fix. Convert the advisory content into mandatory, audited security baselines for power, banking, telecom and healthcare operators under the Information Technology Act, 2000.
    2. Compute dependence caps defensive AI: Running real time detection models at national scale needs domestic graphics processing unit capacity that India does not have. Eg. India’s shortfall spans foundational models, chip design and large scale data centre infrastructure alike. Fix. Prioritise sovereign compute for security workloads specifically, reserving a share of publicly funded AI infrastructure for CERT-In and sector CSIRTs.
    3. Attribution is harder when the attacker is an agent: An AI orchestrated campaign leaves a machine’s traces rather than an operator’s, which weakens the evidentiary basis for a state response. Eg. The GTG-1002 campaign was identified by the model provider, not by a victim’s own forensics. Fix. Mandate model providers serving Indian users to report detected misuse of their systems for offensive operations, on the six hour breach reporting model already in force.
    4. Legacy industrial systems cannot be patched quickly: Control systems in refineries and grids run on decade old software where a patch requires a plant shutdown. Eg. A 27 year old OpenBSD flaw survived in software widely used to run firewalls and critical infrastructure. Fix. Require network segmentation and one way data diodes between industrial control networks and corporate networks, so an unpatched system is not internet reachable.
    5. The skills base is thin at the state level: Cyber investigation and forensics capacity is concentrated in central agencies, while most first response happens at state police stations. Eg. Citizen fraud complaints route through the national helpline before reaching local police with the capacity to act. Fix. Establish State Computer Emergency Response Teams and cyber forensic laboratories with dedicated cyber police training academies in every State.

    Conclusion

    AI has moved cyber conflict from a contest between attackers and defenders to a contest between countries that build AI and countries that buy it. India sits on the wrong side of that line while carrying one of the world’s largest attack volumes, from a ransomware posting of Kudankulam plant data to state backed targeting of its defence establishment. India cannot build the AI stack quickly, so the immediate requirement is that AI and cybersecurity stop being treated in silos and are handled as interconnected strands of policymaking: AI for cyber defence, and cybersecurity for AI.

    Matching Previous Year Question

    “[2022, GS3, 10 marks] What are the different elements of cyber security? Keeping in view the challenges in cyber security, examine the extent to which India has successfully developed a comprehensive National Cyber Security Strategy.”

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