As artificial intelligence (AI) tools provide troops and commanders with more and better decision-making capabilities in the field, they must be kept secure to ensure they are providing the best possible information.
Breaking Defense spoke with Mandy Satterwhite, Managing Director and Cyber Lead at Accenture Federal Services, to explore how operational cyber resilience and trustworthy AI are protecting warfighters and the national security enterprise under adversarial pressure.
Breaking Defense: AI promises to accelerate decision-making across the battlespace, but in high-consequence military operations, speed is useless without trust. What does “trustworthy AI” look like when every second counts on the frontline?
Satterwhite: Trust isn’t an abstract ethical debate — it’s a non-negotiable operational requirement. In the heat of battle, if an AI system relies on poisoned data, yields an unexplainable result, or silently fails behind the scenes, it doesn’t give you an advantage; it creates unacceptable operational risk.
To work for the warfighter, an AI system must be predictable, verifiable, explainable, and resilient under adversarial attack. When command-and-control systems synthesize options for a decision-maker, that leader must know with absolute certainty that those recommendations are built on authoritative, untampered data under our control. If you can’t trace data provenance or explain how the conclusion was derived, you can’t act on it in combat because lives are at stake.
You mentioned data provenance. How much of AI security is a supply chain problem, especially when defense leaders look to use commercial foundation models?
There are very real supply chain challenges to consider. When you take a commercial frontier model off the shelf and fine-tune it, you inherit the data and potential vulnerabilities with it. If the foundational model was compromised or trained on poisoned data, you’re importing that risk straight into your secure environment.
Another consideration that we’re talking with clients about: the larger (and expensive) frontier models often aren’t necessary for specialized military capabilities. Right-sizing the model for specific mission use cases and auditing the building blocks — from data sources to model weights — is essential. We’ve seen adversaries attempt to manipulate model weight files directly to skew outputs. If you don’t control and verify the digital supply chain from end to end, you are building on shaky ground.
Traditionally, cybersecurity compliance meant running a periodic penetration test or going through a pre-deployment checklist. Does that approach still work when applied to AI systems?
Frontier models like Mythos have completely collapsed the cyber-attack timeline. That means periodic testing is dead. Adversaries are constantly probing for vulnerabilities, from prompt jailbreaks designed to exfiltrate sensitive data to sophisticated weight manipulation. We can no longer secure our systems with just a quarterly pen test.
Cyber defense has to move to continuous, automated validation, similar to the concept of “fight fire with fire.” We need to embed AI-enabled red-teaming and vulnerability scanning directly into our continuous integration and continuous delivery (CI/CD) software pipelines. As we develop code, models, and agentic workflows, automated security tools continuously simulate real‑world attack scenarios, enabling us to validate the system harness, operational scaffolding, and model behavior simultaneously.
For the national security ecosystem, we build these pipelines with NIST’s Open Security Controls Assessment Language (OSCAL) embedded directly into the build process. We check against strict security controls in real-time, ensuring systems are truly “secure by design” before they ever touch an operational boundary.
There’s a common perception in defense acquisition that adding layers of cybersecurity inherently slows down operations. How do you balance rigorous security with the need for speed?
The idea that security and speed are mutually exclusive is the biggest misconception in the industry. When security is treated as an afterthought or a final inspection at the end of a multi-year development cycle, yes, it halts progress and causes massive delays.
But when you bake security directly into the architecture from day one, security becomes an accelerator. By continuously validating posture while you build, you eliminate costly late-stage redesigns and accredit systems faster. Designing securely on the front end is what allows you to deploy and pivot at operational speed without fighting blind.
As these automated systems become more capable, how do you preserve human authority without throttling the speed advantage that AI provides?
Our philosophy is anchored in design for augmentation, rather than total automation. AI is built to expand human ingenuity, not replace warfighter judgment.
In sensitive warfighting environments, keeping a human decision-maker in the lead is paramount. That means engineering explicit architectural guardrails, OODA-loop feedback checkpoints, and hard kill switches directly into the system architecture. An AI model can rapidly process unstructured sensor data and surface three actionable courses of action, but a human operator must evaluate those verified choices at a deliberate checkpoint before execution. That structure buys down risk, protects mission integrity, and ensures command authority remains exactly where it belongs.
