Technology – Page 3 – Air Marshal's Perspective

September 9, 2026September 8, 2026

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: –

SAE International. “Guidelines for development of civil aircraft and systems”, SAE Aerospace Recommended Practices ARP4754B, 2023.

International Civil Aviation Organisation. “Cybersecurity action plan”, 2022.

Rose, S., Borchert, O., Mitchell, S., & Connelly, S., “Zero trust architecture”. Special Publication 800-207, National Institute of Standards and Technology, 2020.

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.

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.

Tabassi, E., “Artificial intelligence risk management framework (AI RMF 1.0)” (NIST AI 100-1), National Institute of Standards and Technology, 2023.

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.

Lee, J. D., & See, K. A. “Trust in automation: Designing for appropriate reliance”, Human Factors, 46 (1), 50–80, 2004.

Indian Computer Emergency Response Team. “Blueprint for reducing exposure and defending against AI-assisted vulnerabilities exploitation in digital infrastructure”, 2026.

Ministry of Defence, Government of India. *Security manual for licensed defence industries, 2025.

September 4, 2026

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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References: –

Institute for Defence Studies and Analyses. (2019). Artificial intelligence and national security: Indian perspectives. Manohar Parrikar Institute for Defence Studies and Analyses.

Rajaraman, V. (2014). Robot soldiers: Artificial intelligence and the future of warfare. Resonance, 19(11), 1037–1048.

Horowitz, M. C. (2016). The ethics & morality of robotic warfare: Assessing the debate over autonomous weapons. Political Science Quarterly, 131(2), 331–351.

Boulanin, V., Saalman, L., Topychkanov, P., Su, F., & Peldán Carlsson, M. (2020). Artificial intelligence, strategic stability and nuclear risk. Stockholm International Peace Research Institute.

Su, F., Wan, W., Saalman, L., & Chernavskikh, V. (2025). Pragmatic approaches to governance at the artificial intelligence–nuclear nexus. Stockholm International Peace Research Institute.

Boulanin, V., Saalman, L., Topychkanov, P., Su, F., & Peldán Carlsson, M. (2020). Artificial intelligence, strategic stability and nuclear risk. Stockholm International Peace Research Institute.

Johnson, J. (2020). Artificial intelligence, drone swarming and deterrence. Journal of Strategic Studies, 43(5), 1–24.
Horowitz, M. C. (2019). When speed kills: Lethal autonomous weapons, deterrence, and stability. Journal of Strategic Studies, 42(5), 764–788.

September 1, 2026

844: AIR DOMINANCE IN A DRONE AGE

Article published in the Sep 26 edition of “Life of Soldier” Magazine

Air power is entering a period of structural transformation. For decades, air superiority was largely defined by advanced fighter aircraft’s ability to defeat enemy aircraft, suppress air defences, and provide freedom of action to other elements of the joint force. The emergence of inexpensive unmanned aerial systems (UAS), autonomous technologies, artificial intelligence (AI), electronic warfare and increasingly networked sensors is challenging that model.

The transformation is not simply about drone proliferation. It is about the emergence of a different architecture for warfare in which crewed and uncrewed platforms, sensors, weapons, communications networks and decision-support systems operate as an interconnected combat ecosystem.

Recent conflicts have demonstrated the operational value of relatively inexpensive drones for intelligence, surveillance and reconnaissance, targeting, precision attack, battle-damage assessment and battlefield communications. At the same time, the increasing use of drones in massed attacks has exposed the economic and operational limitations of traditional air-defence concepts.

For air forces, the strategic challenge is therefore two-sided. They must develop the means to exploit unmanned systems. At the same time, they have to protect their own forces and infrastructure from increasingly capable autonomous and semi-autonomous threats.

Autonomous Combat Ecosystem

The traditional air forces were organised around platforms. Fighter aircraft, bombers, airborne early-warning aircraft, tankers and transport aircraft each performed specialised missions. Their effectiveness depended heavily on their individual capabilities and the quality of the personnel operating them.

The drone age is accelerating a shift toward network-centric and distributed operations. A future combat aircraft may simultaneously function as a sensor, command node, communications relay and weapons platform. An uncrewed aircraft operating ahead of it may extend its sensor coverage. Another one may perform electronic warfare. A third may act as a decoy, while a fourth carries weapons. Ground-based radars and space-based sensors can contribute additional information to the same operational picture.

The objective is not necessarily to replace expensive crewed aircraft with drones. Rather, it is to create a force mix in which high-end platforms are supported and amplified by larger numbers of lower-cost autonomous or remotely operated systems. Such an approach could increase the number of sensors, weapons and potential targets available to a commander without proportionally increasing the number of pilots placed at risk.

Future Air Superiority Models

The future air-superiority model is likely to consist of several overlapping components rather than a single dominant capability.

The Manned-Unmanned Team. Advanced crewed aircraft will increasingly function as command-and-control nodes for uncrewed systems. Instead of sending a pilot into every high-risk environment, commanders can distribute risk among a mixture of platforms. This would allow expensive crewed aircraft to remain focused on missions requiring human judgement while autonomous systems provide additional mass.

Distributed Air Power. Future air forces will need to operate despite attacks on bases, runways and communications infrastructure. Dispersal, mobility and rapid reconstitution will therefore become increasingly important. The relevant question will not simply be whether an air force can generate sorties under ideal conditions. It will be whether it can continue generating combat power after its infrastructure has been attacked.

Persistent Sensing. Small unmanned platforms can provide persistent surveillance at relatively low cost. When integrated with ground, maritime, space and airborne sensors, they can contribute to a continuous picture of the battlespace. This creates a paradox for conventional military operations: greater visibility can improve targeting, but it also makes concealment increasingly difficult. Camouflage, deception, emissions control and mobility will therefore become more important, even as sensor technology improves.

Information and Electronic Superiority. Air superiority increasingly depends on the ability to sense, communicate and coordinate. An aircraft with excellent aerodynamic performance that cannot maintain communications or trust its sensor information may be comparatively less effective. Electronic warfare will therefore become an integral component of air combat rather than a specialist supporting function. The battle for the electromagnetic spectrum will increasingly accompany the battle for physical airspace.

Air Defence Against Swarms

Drone swarms represent one of the most difficult challenges for contemporary air defence because they combine low cost, numbers, flexibility and uncertainty. A conventional air-defence architecture may be highly effective against a limited number of sophisticated threats yet struggle economically when confronted by large numbers of inexpensive UAS. The problem is fundamentally one of cost-benefit comparison.

This makes layered defence essential. The first requirement is detection. Traditional radar must increasingly operate alongside electro-optical, infrared, acoustic and other sensing technologies. The objective is to build a multi-sensor picture that can identify small, slow-moving objects.

The second requirement is automated classification. A large raid can generate more tracks than human operators can efficiently evaluate. AI-assisted systems may therefore become essential for filtering sensor data and prioritising potential threats.

The third requirement is a response mechanism. Different threats require different responses. Expensive interceptors may be used against high-value, sophisticated targets. Low-cost defensive systems can address simpler threats.

Electronic warfare provides another layer. Other technologies, including directed-energy systems, may eventually augment the defensive options where operational conditions permit.

The key principle is that air defence against swarms cannot depend upon a single weapon. It must be an integrated system of sensors, command networks, electronic warfare and multiple classes of interceptors.

Adaptation Trends

Air forces worldwide are fundamentally redesigning how they conceive, plan, and execute air operations. Air forces are increasingly adapting their force structures around three major developments: distributed operations, manned-unmanned teaming and rapid technological adaptation.

Distributed operations seek to reduce dependence on a small number of vulnerable air bases and command nodes. A concentrated air force may possess extremely capable aircraft but still face operational paralysis if its runways, fuel infrastructure, command centres or communications nodes are disabled. Aircraft, sensors, logistics and command functions need to be dispersed across a wider geographical area. This complicates an adversary’s targeting problem and increases the force’s resilience. The concept is particularly relevant in an era of long-range precision weapons.

Manned-unmanned teaming is another trend. A future fighter may coordinate multiple uncrewed systems rather than operating alone. These platforms could undertake sensing, communications, electronic warfare, decoy and other missions while keeping human decision-makers at an appropriate level of control. This approach effectively increases the combat mass available to each crewed platform.

The third transformation is the increasing importance of software and data. Aircraft increasingly depend on digital mission systems, secure communications, electronic warfare databases and AI-enabled decision support. The ability to update these capabilities rapidly can become a strategic advantage.

This creates a different procurement philosophy. Rather than treating an aircraft as a fixed capability acquired for several decades, air forces increasingly need platforms that can accept frequent software, sensor and weapons upgrades.

Analytical Perspective

Extent of Autonomy. Autonomy is likely to be a defining feature of future air warfare, but it should not be confused with the complete removal of humans from the kill chain. The more consequential development is the creation of autonomous combat ecosystems in which machines perform increasingly complex supporting tasks while humans retain command authority over critical decisions. AI can process enormous quantities of sensor data, identify patterns, prioritise potential threats and provide recommendations to operators. Autonomous systems can maintain formations, navigate contested environments, coordinate movement and respond to changing conditions within pre-established parameters. The military advantage comes from speed. Modern battlespaces can generate more information than human operators can absorb. A system that can process multiple sensor feeds simultaneously may identify an emerging threat far faster than a human-centred process.

New Vulnerabilities. Autonomy also creates new vulnerabilities. A sophisticated autonomous system depends upon software, communications, navigation and data. These dependencies create opportunities for cyber-attack, electronic warfare and deception. An adversary may attempt to corrupt the information an autonomous system relies on rather than physically destroying the platform. The future contest will therefore involve not only physical survivability but also algorithmic and informational survivability. Military organisations will need confidence that autonomous systems can continue operating when communications are degraded, GPS or other navigation services are disrupted, and sensor information becomes incomplete or contradictory. The most resilient systems may therefore be those that can operate with degraded connectivity while retaining clear human-defined constraints and mission objectives.

The Industrial Scalability. The drone age is making industrial capacity a key part of military power. A traditional air force measures strength through the number and sophistication of aircraft in its inventory (Quantity and Quality). Future conflicts would require scalability: the ability to rapidly manufacture autonomous systems, replenish losses, update software, produce sensors, and maintain secure communications. Modular design, commercial technology, software-defined systems and rapid manufacturing could allow air forces to shorten development cycles.

The Continuing Role of the Fighter. The rise of drones should not be interpreted as the end of the fighter aircraft. High-performance crewed aircraft continue to provide capabilities that autonomous systems cannot easily replicate across every mission. Human judgement, flexibility, situational awareness and the ability to respond to unexpected circumstances remain important. The change is that the fighter may no longer operate as an isolated platform, but as part of a networked ecosystem. Integrating the fighter into a wider ecosystem would make it more valuable, not less relevant.

Concluding Thoughts

This transformation highlights several important aspects.

Mass is once again a strategic necessity; even though high-end capability matters, affordable mass can impose excessive operational and economic costs on the opponent.

Resilience is as important as performance. Networks, bases and command systems must be capable of absorbing disruption.

Software and data are becoming combat capabilities rather than merely supporting functions.

Neutralising the swarm’s ability to coordinate is more important than physically destroying every individual drone.

Air defence must evolve from a capability to merely intercept platforms to disrupting the networked operating systems.

Human-machine integration will define command and control. The objective should not be unrestricted autonomy but the intelligent allocation of tasks between humans and machines.

Bottom Line

The future of air dominance will consequently be determined less by individual platforms and more by the ability to integrate manned aircraft, autonomous systems, sensors, networks, electronic warfare and air defence into a resilient force.

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References: –

Dhakate S, “Synergistic applications of autonomous UCAVs, swarm robotics and cloud computing in future air warfare”, Journal of Defence Studies, 17(4), (2023).

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