Building Reliable AI Systems for Defence Applications
Mission-critical environments demand a different approach to AI deployment. Lessons learned from building systems where failure is not an option.
Turing Labs Team
AI Engineering
Defence applications represent the extreme end of AI reliability requirements. When we build systems for this sector, we operate under constraints that would be considered excessive in commercial contexts—and that's precisely the point.
The Reliability Imperative
In commercial applications, a 95% accuracy rate might be celebrated. In defence contexts, we think about the 5% differently. What happens in those failure cases? Are they random, or do they cluster around specific scenarios? Could an adversary exploit these failure modes?
Our approach begins with extensive failure mode analysis before writing a single line of model code. We map every possible way the system could fail, assess the consequences of each failure type, and design mitigations accordingly.
Designing for Degradation
Robust defence systems must degrade gracefully. We build multi-layered architectures where the failure of any single component doesn't cascade into system-wide failure. This means redundant models, fallback logic, and clear escalation paths to human operators.
A surveillance system we developed maintains three independent detection models trained on different data subsets and architectures. Consensus requirements vary by threat level—in high-stakes scenarios, all three must agree before automated response; in routine monitoring, majority rules with flagged disagreements.
Adversarial Robustness
Unlike commercial AI, defence systems must assume intelligent adversaries actively seeking to deceive them. We subject every model to extensive adversarial testing: input perturbations, data poisoning scenarios, model extraction attempts, and more.
This testing regularly reveals vulnerabilities invisible to standard evaluation. A classifier achieving 99% accuracy on held-out test data might drop to 60% under carefully crafted adversarial inputs. We build defences into the architecture: input validation, anomaly detection on model inputs, and ensemble methods that resist targeted attacks.
Operational Security
The models themselves are assets requiring protection. We implement strict access controls, audit logging, and monitoring for unusual query patterns that might indicate extraction attempts. Model updates follow change management processes with rollback capabilities.
Human-Machine Teaming
Perhaps counterintuitively, the most reliable defence AI systems are those designed around human oversight. We build interfaces that present not just predictions but confidence levels, supporting evidence, and potential alternative interpretations. Operators maintain meaningful control, and the system supports rather than supplants their judgement.
The Certification Challenge
Defence procurement requires extensive documentation and certification. We've developed practices that generate compliance artifacts as a byproduct of development: automated test reports, model cards, and audit trails that satisfy regulatory requirements without separate documentation efforts.
Building AI for defence isn't about achieving the highest benchmark scores—it's about building systems that perform reliably under conditions that would break typical commercial deployments.