Category: Cyber

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849: ENGINEERING TRUST IN THE DIGITAL AEROSPACE ERA

 

Shared my views on the subject at the DSCI Summit

on 09 Sep 26

 

 

Aerospace is undergoing the deepest transformation since the shift from piston to jet propulsion, except that this one is digital rather than mechanical. Aerospace systems are becoming software-defined, networked, and AI-enabled.

Flight control laws, mission systems, maintenance procedures, and command networks are increasingly mediated by software, sensors, and, increasingly, machine learning models.  These models can make decisions or influence them at a speed no human operator can independently verify in real time.

In an era where aerospace systems are defined as much by lines of code as by laws of aerodynamics. A compromise of software, data or communications can have consequences comparable to physical damage. In this environment, the “Engineering Trust” is taking on a new connotation.

 

Transformation of the Definition of “Engineering Trust”.

Engineering trust means designing, testing, and building systems so predictably safe and transparent that humans and regulators can rely on them without hesitation. The digital aerospace era has transformed the scope of “engineering trust”. Beyond the stated definition, it now also includes “having complete confidence that digital tools, data networks, and artificial intelligence (AI) systems are safe, secure, and fully verified”.

Trust must therefore extend from the physical platform to code, data, identity, networks, AI and the supply chain. It is no longer merely a peripheral technical concern. Engineering trust has become a central focus. Engineering digital trust requires validating each link, including hardware, flight software, network communications, and operational execution. It must be demonstrated, verified, and maintained throughout the system lifecycle.

The challenges relate to both the software code and the Data.

    • Code-related matters emphasise the importance of trust, Verification, authentic updates, and isolating compromised software to enhance resilience.
    • Data-related matters include data poisoning, Model manipulation, Adversarial inputs, AI Hallucination, unreliable outputs, and supply-chain digital compromises.

 

Core Pillars of Digital Aerospace Trust

Safety, Reliability, and Airworthiness. Safety, reliability, and airworthiness require that digital systems (such as flight controls, mission systems, avionics, MRO platforms, and autonomous functions) act predictably in normal, failed, and off-nominal situations without compromising platform or mission safety.

Cybersecurity and Cyber Resilience. The term cybersecurity, with an expanding scope, has been transformed into “digital mission assurance”. Aircraft, satellites, ground systems, and supporting infrastructure must be protected against cyber threats. Protective systems must prevent, detect, contain, withstand, and recover from attacks. This protection is required across the product lifecycle and supply chain.

Software Assurance and Verification. The development, verification, and certification of flight-critical software must be thorough and should follow the certification procedures adopted by aviation regulatory bodies such as the FAA and EASA. Formal methods and mathematical verification can complement testing and provide greater assurance of correctness, especially for functions that require a high level of assurance. Assurance must also extend to software updates, digital twins, and the growing number of adaptive systems.

Data Integrity and Sovereignty. Ensuring data integrity and sovereignty means that navigation, telemetry, sensor, maintenance, and operational data must be accurate, genuine, and readily available. It must also be protected against manipulation or spoofing.  Solid data pipelines, anti-spoofing methods, and sensor integration (for example, combining GNSS, INS, and optical tracking) can increase resilience. Regarding data sovereignty, it is also necessary to control where the data is stored, who can access it, and which legal or regulatory system applies, especially for multinational and classified programs.

AI Trustworthiness and Bounded Autonomy. AI/ML is used in predictive maintenance, decision support, pilot assistance, and autonomous systems. In these cases, evidence of robustness, transparency, accountability, and suitable human oversight is required. Autonomous features must operate within clearly defined and verifiable safety limits; deterministic safety mechanisms or “wrappers” (protective software boundaries or guardrails that surround an adaptive AI algorithm to restrict its behaviour and ensure safety) should keep adaptive algorithms within their certified operational envelopes.

Human–Machine Trust. The trust humans place in automation requires that pilots, engineers, and operators have enough insight into the system’s status, limitations, uncertainty, and decision-making logic to know when to rely on the automation and when to intervene or take over.

Supply-Chain and Firmware Integrity. Trust must extend throughout the entire manufacturing process, from component sourcing to software development, integration, deployment, and maintenance.

Continuous Lifecycle Assurance.  Trust cannot be established once and then assumed; it must be maintained through configuration control, monitoring, vulnerability management, software updates, supplier changes, operational data, and evidence provided throughout the entire system lifecycle.

 

Policy Framework for Engineering Trust in the Digital Era

Appropriate measures are required to ensure that India’s digital ecosystem for the aerospace and defence sector is secure, sovereign, traceable, certifiable and internationally trusted. The framework should contribute to national security, speed up the adoption of digital engineering and AI, improve supply chain resilience, support certification and exports, and, at the same time, increase confidence in India’s expanding indigenous aerospace and defence industrial base.

Make Digital Engineering Safe and Trustworthy. Set up a reliable digital engineering framework for the aerospace and defence sectors to guarantee the safety, reliability, security, and integrity of digital models, software, hardware, and digital twins. Adopt internationally recognised aerospace standards where appropriate and create end-to-end digital traceability covering the entire process from requirements, through design, implementation, verification, to certification. At the same time, maximise the automation of verification and testing and incorporate cybersecurity throughout the entire engineering lifecycle.

Protect Manufacturing and MRO from Cyber Attacks. Build robust and cyber-secure manufacturing, maintenance, repair and overhaul (MRO), testing, and operating environments using zero-trust principles. Ensure strong identity management, multi-factor authentication, least-privilege access, network segmentation, continuous monitoring, and strict third-party controls are in place. Cybersecurity requirements must cover the entire supply chain, including measures to ensure production and maintenance can continue during a cyber incident.

Make AI Safe and Trustworthy. We should establish a governance and assurance framework so that AI may be safely introduced into the aviation and defence sectors. Clear boundaries must be set regarding the level of autonomy of AI, and there must be adequate human supervision for any decisions which are of safety or mission importance. AI systems must be thoroughly tested under normal, abnormal, and failure conditions, monitored throughout their operational life, and supported by appropriate measures for accountability, data provenance, transparency, security, and auditability.

Create Digital Traceability for Parts and Products. Set up a complete digital traceability system for important aerospace and defence parts and components. Give each item a unique digital identity and keep reliable records relating to its origin, certification, configuration, inspections, repairs, modifications, and full lifecycle history. Apply tamper-evident technologies and digital product passports, as appropriate, in order to enhance authenticity, prevent the use of counterfeit components, and meet regulatory and customer assurance requirements.

Control Sensitive Data and use the Sovereign Cloud. Establish a national framework which covers the classification, protection, storage, processing, and controlled sharing of sensitive, classified, proprietary and export-controlled information. Sensitive aerospace and defence data should remain under the proper control of the nation and the organisation. For important programmes, use sovereign or controlled cloud environments, while allowing secure international cooperation without jeopardising sensitive data or intellectual property.

Develop Certifiable Digital Engineering Capability. Build national and industrial capabilities in the areas of model-based engineering, model-based systems engineering, and digital twins. Ensure digital engineering practices conform to international aerospace standards and automate verification where possible. Create definitive digital evidence to support airworthiness certification, obtain regulatory approval, and gain international customer acceptance.

Set up a Zero-Trust Defence Industrial Base. Introduce a zero-trust approach to cybersecurity in all defence organisations and within their industry ecosystem. Protect critical networks and systems by implementing robust identity management, using multi-factor authentication, applying segmentation, applying the principle of least privilege, and maintaining continuous monitoring. Make cybersecurity assurance a fundamental requirement of defence procurement and supplier management, backed by a common framework for managing cyber risk across the defence industrial base.

Develop sovereign AI and Cloud Capabilities. Create sovereign AI and cloud infrastructure for sensitive aerospace and defence applications, ensuring the nation maintains control over critical data, models, infrastructure, and intellectual property. Develop secure AI capabilities for areas including UAVs, predictive maintenance, mission support, simulation, and other defence applications, while allowing controlled collaboration with international partners.

Establish Governance for AI in Safety-Critical Systems. A governance protocol is required for using AI in safety-critical systems.  It should contain clearly defined sector-specific rules.  It should also include requirements for human supervision, intervention, testing, monitoring, accountability, and assurance throughout the system lifecycle. The governance framework must align with established principles of trustworthy AI, including safety, security, transparency, privacy, fairness, robustness, and accountability.

 

Concluding Thoughts

A digital platform, network, or system that you do not trust should not be considered a capability but rather a liability. The principle in question—that trust is essential—has not changed with digitalisation; in fact, digitalisation has made it harder and more urgent to engineer trust.

Trust in the digital aerospace era should not be treated as a secondary issue or as a compliance item to be dealt with after the project has been completed; instead, it must be built into the system from the very first line of code, the very first component, and the very first hour of operator training. It must be maintained throughout the system’s entire lifecycle.

Engineering trust in the digital era is therefore not merely about aerospace system reliability. It is about ensuring that their safety and security are continuously demonstrable, auditable, and verifiable.

 

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Information and data included in the blog are for educational & non-commercial purposes only and have been carefully adapted, excerpted, or edited from reliable and accurate sources. All copyrighted material belongs to the respective owners and is provided only for wider dissemination.

 

 

References: –

  1. SAE International. “Guidelines for development of civil aircraft and systems”, SAE Aerospace Recommended Practices ARP4754B, 2023.
  1. International Civil Aviation Organisation. “Cybersecurity action plan”, 2022.
  1. Rose, S., Borchert, O., Mitchell, S., & Connelly, S., “Zero trust architecture”. Special Publication 800-207, National Institute of Standards and Technology, 2020.
  1. Boyens, J., Smith, A., Bartol, N., Winkler, K., Holbrook, A., & Fallon, M. “Cybersecurity supply chain risk management practices for systems and organisations”. Special Publication 800-161 Rev. 1, Update 1, National Institute of Standards and Technology, 2024.
  1. National Institute of Standards and Technology. “Secure software development practices for generative AI and dual-use foundation models”, NIST AI 600-1, supplementary guidance, 2024.
  1. Tabassi, E., “Artificial intelligence risk management framework (AI RMF 1.0)” (NIST AI 100-1), National Institute of Standards and Technology, 2023.
  1. Autio, C., Schwartz, R., Dunietz, J., Jain, S., Stanley, M., Tabassi, E., Hall, P., & Roberts, K., “Artificial intelligence risk management framework: Generative artificial intelligence profile”, NIST AI 600-1, National Institute of Standards and Technology, 2024.
  1. Lee, J. D., & See, K. A. “Trust in automation: Designing for appropriate reliance”, Human Factors, 46 (1), 50–80, 2004.
  1. Indian Computer Emergency Response Team. “Blueprint for reducing exposure and defending against AI-assisted vulnerabilities exploitation in digital infrastructure”, 2026.
  1. Ministry of Defence, Government of India. *Security manual for licensed defence industries, 2025.

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847: DETERRENCE IN THE AUTONOMOUS WARFARE SCENARIO

 

Article published in the Sep 26 edition of “News Analytics” magazine

 

The character of warfare evolves alongside technological innovation. Today, another transformative revolution is reshaping global security.  The integration of artificial intelligence (AI), autonomous weapons systems, and human-machine teaming into military operations is underway.

Deterrence has traditionally depended on a human adversary believing that the costs of aggression will outweigh its expected benefits. Nuclear weapons made this logic especially powerful by creating the prospect of unacceptable retaliation.

Autonomous warfare, however, is altering the speed, visibility and psychology of military competition. Artificial intelligence-enabled systems can detect, classify, track and engage targets with limited human intervention. In contrast, autonomous drones, loitering munitions, cyber tools, underwater vehicles and defensive systems may operate across several domains simultaneously. The central question is whether autonomous systems will replace conventional or nuclear deterrence or alter its dynamics.

 

The Transformative Effects on Deterrence

At the core of this transformation is the integration of AI across the full spectrum of military functions, including intelligence, surveillance, and reconnaissance (ISR), targeting, command and control, logistics, and, increasingly, the application of force. Autonomous platforms (ranging from loitering munitions and drone swarms to AI-enabled decision-support systems) operate with varying degrees of independence once activated. Human-machine teaming seeks to combine the strengths of both.

Autonomous systems are particularly suited to denial because they can support persistent sensing, distributed defence, and rapid disruption. Unmanned aerial vehicles can monitor approaches to military installations; autonomous underwater systems can observe maritime activity; ground robots can support border surveillance; and AI-enabled command systems can fuse information from multiple sensors. A networked force may complicate an adversary’s effort to achieve surprise, suppress defences or destroy high-value targets.

Autonomy can enhance capability by allowing military systems to process enormous quantities of data and act faster than human operators. An AI-enabled surveillance network may identify changes in an adversary’s deployment patterns before traditional intelligence systems do. Autonomous platforms can then maintain continuous patrols, coordinate with one another and respond to selected threats without waiting for detailed instructions. Such capabilities may strengthen deterrence by denial.

The importance of denial will increase as military forces become more dispersed. Instead of protecting a small number of vulnerable platforms, states may deploy large numbers of mobile, concealed and networked systems. An adversary would then face a difficult targeting problem: destroying some autonomous platforms would not necessarily turn off the entire force. This resilience can reduce the attractiveness of a first strike.

Autonomy can influence credibility. A state with resilient, dispersed, and relatively inexpensive autonomous systems may be able to respond to aggression even after suffering damage to its command centres, air bases, or naval facilities. Swarms and unmanned systems can generate operational effects without exposing large numbers of personnel to danger. This may make retaliation more politically acceptable and therefore more credible.

Autonomy can also impose operational costs. Defensive systems may use algorithms to detect incoming missiles, drones or aircraft and recommend or initiate responses. If these systems are reliable and their employment conditions are clearly defined, they can reduce the prospect that an adversary will achieve a quick victory. The strategic message becomes: aggression will encounter a persistent, adaptive and difficult-to-suppress defence.

 

Escalation Dynamics

The most serious challenge is compressed decision time. Autonomous systems can identify and respond to threats faster than human institutions can verify information, consult political leaders or establish whether an incident was deliberate. In a crisis, this may generate a “use-or-lose” mentality. Commanders may fear that delaying action will allow an adversary’s autonomous systems to destroy their own sensors, communications or retaliatory forces.

Machine-speed operations can therefore create pressure for pre-delegation. Political and military leaders may authorise automated responses in advance because human approval would be too slow. Yet pre-delegation carries significant risks. An algorithm may misinterpret a civilian aircraft, a training exercise or a cyber intrusion as an attack. A technical malfunction could trigger a chain of responses that neither side intended.

Autonomous warfare also creates the possibility of machine-to-machine interaction. One state’s defensive algorithm may classify the activation of another state’s autonomous system as hostile. The second state may then respond automatically, producing reciprocal escalation. Unlike human decision-makers, algorithms do not possess political intuition, historical memory or an inherent preference for restraint. They optimise according to programmed objectives and available data. If the data are incomplete or manipulated, the resulting decision may be technically rational but strategically disastrous.

 

Strategic Stability

Autonomous warfare will have its most consequential impact on strategic stability when it interacts with nuclear forces. AI-enabled systems may improve early warning, intelligence analysis and the protection of nuclear assets. They could help identify suspicious activity and strengthen command-and-control resilience. In this respect, autonomy may reduce uncertainty and support more informed decisions.

The opposite is also possible. AI-enabled surveillance and autonomous strike systems could threaten mobile missiles, submarines, command centres and communications networks. If one state believes that its nuclear deterrent is becoming vulnerable, it may adopt more aggressive readiness postures or delegate greater authority to military commanders. The combination of improved detection, persistent tracking and rapid attack could generate fears of a disarming first strike.

This danger is not limited to actual technical capability. Perception also does matter. A state may respond to what it believes an adversary can do, even if the adversary’s systems are not as effective as assumed. Strategic competition could consequently become unstable through exaggerated assessments of AI-enabled counterforce capabilities. The combination of advanced intelligence, strike systems and missile defence may challenge the traditional assumption that retaliation remains assured after a first strike.

Autonomous systems may also blur the boundary between conventional and nuclear operations. An attack on dual-use command-and-control infrastructure by conventional autonomous weapons could be interpreted as preparation for a nuclear attack. If a state cannot distinguish between an anti-conventional operation and an anti-nuclear operation, it may escalate rapidly. Maintaining clear separation between nuclear and conventional assets, preserving human decision authority and establishing crisis communication channels will therefore become increasingly important.

 

Implications for India and Regional Security

For India, autonomous warfare is directly relevant to a security environment characterised by contested borders, maritime competition, terrorism, cyber threats, and the possibility of simultaneous pressure from multiple directions. Autonomous systems can enhance surveillance along difficult terrain, improve maritime domain awareness and support the protection of critical infrastructure. They can also strengthen deterrence by denial by making surprise incursions, drone attacks and limited aggression more difficult to execute successfully.

However, technology alone cannot provide deterrence. India would require an integrated architecture combining sensors, secure communications, electronic warfare, cyber resilience, air defence, space-based support and trained human operators. Autonomous platforms must remain connected to a wider command-and-control system that can function even when communications are degraded, or networks are attacked.

 

Concluding Thoughts

The transition to algorithm-assisted autonomous warfare will not change the basic concept of strategic deterrence. It will mandate a more controlled version of it. One that accounts for machine-speed decision cycles and distributed, ambiguous chains of responsibility. It will also have to cater for an adversary whose calculations are also based on software architectures. Autonomy will create an additional layer of deterrence, one based on persistent surveillance, rapid response, distributed force structures, denial of objectives and the ability to impose costs at machine speed.

As autonomous capabilities continue to grow, policymakers will have the important task of adapting and finding balance during this transition. In autonomous warfare, deterrence will depend not just on cutting-edge technologies but also on ensuring these systems operate within clear, transparent guidelines, robust command structures, well-rounded legal frameworks, and internationally accepted norms. The goal isn’t to slow technological progress, but to ensure that humans, who are ultimately responsible for warfare, still have the power to choose peace.

 

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Disclaimer:

Information and data included in the blog are for educational & non-commercial purposes only and have been carefully adapted, excerpted, or edited from reliable and accurate sources. All copyrighted material belongs to the respective owners and is provided only for wider dissemination.

 

 

References: –

  1. Institute for Defence Studies and Analyses. (2019). Artificial intelligence and national security: Indian perspectives. Manohar Parrikar Institute for Defence Studies and Analyses.
  1. Rajaraman, V. (2014). Robot soldiers: Artificial intelligence and the future of warfare. Resonance, 19(11), 1037–1048.
  1. Horowitz, M. C. (2016). The ethics & morality of robotic warfare: Assessing the debate over autonomous weapons. Political Science Quarterly, 131(2), 331–351.
  1. Boulanin, V., Saalman, L., Topychkanov, P., Su, F., & Peldán Carlsson, M. (2020). Artificial intelligence, strategic stability and nuclear risk. Stockholm International Peace Research Institute.
  1. Su, F., Wan, W., Saalman, L., & Chernavskikh, V. (2025). Pragmatic approaches to governance at the artificial intelligence–nuclear nexus. Stockholm International Peace Research Institute.
  1. Boulanin, V., Saalman, L., Topychkanov, P., Su, F., & Peldán Carlsson, M. (2020). Artificial intelligence, strategic stability and nuclear risk. Stockholm International Peace Research Institute.
  1. Johnson, J. (2020). Artificial intelligence, drone swarming and deterrence. Journal of Strategic Studies, 43(5), 1–24.
  2. Horowitz, M. C. (2019). When speed kills: Lethal autonomous weapons, deterrence, and stability. Journal of Strategic Studies, 42(5), 764–788.
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823: Wings of Dominance: The Future of Air Warfare

 

Q1.  What is the new balance of air power in the world today? Are fighter jets still the focus of warfare, or are drones beginning to take their place?

Fighter jets remain the backbone of air power, and that is not about to change. What has changed fundamentally is the ecosystem around them. A modern fighter operates in a networked environment comprising long-range strike weapons, unmanned systems, loitering munitions, airborne tankers, and space-based ISR.

Drones are taking over the missions that are too risky, too repetitive, or too economically unjustifiable to warrant a manned sortie. They are not replacing the manned aircraft.

The prevailing trend favours a combination of manned and unmanned systems. Manned aircraft are focusing on contested, high-end missions that require judgment, adaptability, and versatile payloads. Concurrently, unmanned systems are being employed in persistent, attritable, and mass-effect roles.

The adaptation to this hybrid model is no longer merely a tactical requirement; it has become a strategic necessity.

 

Q2.  Russia’s Su-57 and the US F-35 embody different philosophies — one emphasises air combat, the other network-centric warfare. Whose future will it be?

The Su-57 seems to reflect the traditional Russian emphasis on kinematic performance and super-manoeuvrability.

The F-35 is claimed to be built around sensor fusion and battlespace awareness. It is advertised as capable of detecting, classifying, and engaging the threat at beyond-visual-range distances through a data architecture spanning an entire networked force.

Future aerial combat is progressing towards a network-centric model. Contemporary air engagements are increasingly determined by the priority of achieving information and decision dominance, rather than by performance alone.

Compressing the sensor-to-shooter timeline is now as critical as speed or manoeuvrability. This is fundamentally a problem of decision architecture, not merely of technology.

The sixth-generation programmes are pushing emerging platforms toward multi-domain integration.  Fusion of air, space, cyber, and electronic warfare into a single operational architecture will make the network-centric model more definitive.

 

Q3.  China already has the J-20. Has India delayed the AMCA too long, or is it still possible to turn the situation around?

It is a fact that India’s timeline has slipped. The J-20 has been operational for nearly a decade. China is already iterating toward a sixth-generation capability, as evidenced by the prototypes that emerged publicly in late 2024.

AMCA is still working through prototype development. The gap is significant and widening. Reversal of the trend is a realistic necessity.

India can recover lost ground in fighter development if the programme is properly resourced, executed and politically backed.

A significant structural shift is also underway with the Ministry of Defence opening AMCA prototype development to private consortia rather than relying exclusively on the public-sector model.

The window to close the capability gap exists. It will not remain open indefinitely, and the margin for complacency on programme management is close to zero.

 

Q4.  In the wars to come, will Artificial Intelligence and Loyal Wingman drones be more important than pilots?

The pilot does not become less important. His job changes, and in some respects becomes more demanding, not less.

Manned-unmanned combat air teams would have one crewed aircraft effectively commanding a tactical formation of attritable unmanned assets, absorbing risk that would otherwise fall on the manned platform, carrying missiles, jammers, decoys, or forward reconnaissance payloads.

What AI is changing is the speed and volume of decision-making below the human threshold.

AI-enabled satellites and sensors, capable of detecting, classifying, and cueing targets, can push that picture directly to the shooter over tactical data links, rather than routing it back through a ground station first. That is what compressing the sensor-to-shooter timeline. However, human intervention cannot be removed from the kill chain.

As of now, the human crew retains authority over decisions that carry lethal and political consequences, while AI absorbs the burden of processing, prioritising, and routing information faster than any human can.

So, AI and unmanned teaming will unquestionably become more important than they are today. But the human crew would remain relevant and in control.

The pilot of 2040 will be managing a far more complex battle picture, commanding a digital wolfpack rather than flying a single aircraft.

 

Q5.  If India has the opportunity to purchase the F-35 or the Su-57, should we go ahead and purchase them, or stick to developing our own aircraft?

These are not competing choices, and treating them as such leads to a false dilemma.

The IAF’s squadron strength shortfall is real, immediate, and strategically significant. The Rafale has helped close that numerical gap, but has not closed it.

Further, there is a case for qualitative enhancement by the induction of fifth-generation aircraft.

The F-35 carries substantial geopolitical weight, end-use restrictions, and software dependency. Cost, delivery timelines, extended supply chains, Transfer of technology and trust deficit are other factors to be taken into account.

Russia has been a trusted partner, willing to share its technology to a certain extent and accepting Make in India. The Su-57 also raises several concerns besides the factors listed above. India had earlier walked out of the co-development program mainly due to concerns related to cost and technology sharing.

Neither platform offers a clean, dependency-free solution. The importance of self-reliance in defence production is a common lesson emerging from recent wars. The Indigenous program (AMCA) is some time away and urgently needs a technology infusion.

The logical answer is to plug the gap pragmatically by expanding the Rafale order and carefully reassessing the induction of fifth-generation aircraft, while protecting AMCA’s funding and schedule as a non-negotiable national priority.

The near-term interim acquisition and the long-term indigenous programme must be advanced concurrently. The contract should be negotiated in a manner that boosts the indigenous programme rather than undermining it.

 

Q6.  Is engine technology still India’s biggest weakness today?

The answer is YES. The Tejas Mark 1A flies on the American GE F404. AMCA’s initial squadrons will likely depend on an imported engine in the ninety-kilonewton class. The latest news is that negotiations for the GE 414 engine for AMCA have hit rough weather due to a 300 per cent cost increase.

India still does not have a proven indigenous engine anywhere near the ninety to one hundred ten kilonewton range required for a credible fifth or sixth-generation fighter. The Kaveri programme, running since the mid-1980s, is the most visible illustration of how difficult this problem is. High-performance turbofan technology demands a combination of high-temperature metallurgy, single-crystal turbine blade manufacturing, precision tolerances, and decades of iterative test data that very few nations have accumulated.

Urgent need of the hour is a deal that includes a degree of co-production and technology transfer for engine manufacturing in India. Co-production extends the supply chain into India, but it does not give India the ability to independently design, test, and certify a clean-sheet high-thrust engine. Engine independence remains the single weakest link in the self-reliance story.

 

Q7.  Will the export of fighter jets become an increasingly important geopolitical tool?

Fighter exports are already an important geopolitical tool, and their leverage is intensifying rather than diminishing.

Fighter exports create decades of dependency for the buyer. The seller retains influence over the buyer’s operational readiness (by supplying spares, software updates, weapons integration, training pipelines, and maintenance protocols). This dependency lasts for the life of the platform (often 30 to 40 years after the sale).

India’s own indigenous push is a deliberate effort to reduce exposure to precisely this kind of dependency.  India’s active promotion of the Tejas and its indigenous missile systems in Southeast Asia, West Africa, and the Gulf reflects a clear understanding that defence exports are as much an instrument of foreign policy as of industrial economics. Future fighter sales will be negotiated as much on reliability of supply and strategic alignment as on cost or raw capability.

 

Q8.  What are India’s greatest achievements and biggest challenges in defence self-reliance?

Tejas moving from a deeply troubled programme to a credible inducted fighter is, to a certain extent, an achievement.  The development of indigenous rotary-wing platforms (Dhruv, Rudra, the Light Combat Helicopter Prachand) demonstrates that the industrial capacity extends beyond fast jets. The Astra beyond-visual-range missile and the continued maturation of the BrahMos supersonic cruise missile represent genuine capability in the weapons domain. The missile and space programs are doing comparatively well.

Perhaps most significantly, India’s defence production turnover has grown substantially over the past decade. The country has moved from being almost exclusively an arms importer to a growing exporter, which is a structural shift that would have seemed improbable fifteen years ago.

The challenges are equally tangible. Squadron strength remains well below the sanctioned forty-two. Force multipliers, tankers, airborne early warning and control platforms are inadequate in numbers for a force that needs to project across two frontiers simultaneously. Engine technology remains unresolved.

The achievements prove India can build technically demanding systems. What remains unproven is whether it can build them at the pace and scale that the threat environment now demands.

 

Q9.  How will the Indian Air Force look in 2040, compared to today?

By 2040, assuming the squadron strength target is met or even meaningfully mitigated, the IAF should be a genuinely different force, operating on a different conceptual basis.

AMCA should be in serial production, forming the high-end backbone alongside an upgraded Rafale fleet and a substantially modernised Su-30MKI. The Tejas Mark 2 and the twin-engine deck-based fighter should round out the order of battle, bringing the indigenous content of the combat fleet to a level inconceivable at the beginning of this decade.

Loyal Wingman and unmanned systems would be standard formation elements rather than experimental adjuncts.

AI-assisted Space-based ISR would be integrated into the network.

The UCAV and other Unmanned platforms will significantly enhance airpower capabilities.

If the present trajectory and pace are sustained, by 2040 the IAF should be more networked, more integrated with the space and cyber domains, and far less dependent on foreign supply chains than anything currently in service.

 

Q10.  If you had to identify one defining trend in air warfare over the next twenty years, what would it be?

The shift from platform-centric to weapon-centric airpower operating in a networked environment. The idea that the decisive factor in air combat is increasingly not which aircraft you fly, but how fast you can sense, decide, and act across a distributed force. Ada result:

The sensor-to-shooter timeline will get shortened further.

Space-based satellites with onboard AI capable of detecting, classifying, and cueing the targets will push that picture directly to the shooter.

Manned and unmanned systems will operate as a single collaborative entity rather than parallel fleets.

Mastery of the electromagnetic spectrum, with digital and cognitive dimensions layered on top, would become essential.

Stealth, hypersonics, manoeuvrability, drone swarms, and directed energy technologies/capabilities would follow this shift.

The air forces that adapt to it early will hold the operational advantage in 2040 and beyond. The ones that keep procuring better individual platforms while neglecting the architecture around them (i.e. modern equipment running on an outdated decision framework) will find themselves technologically current but operationally lagging.

 

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