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Start with the mission and intended-use boundary
Before selecting or building a system, write down what it is meant to do and what it must not do. A predictive model that prioritizes maintenance alerts and a generative system that summarizes operational reports have different users, data flows, failure consequences, and attack surfaces. Security controls should reflect those differences.
For each proposed capability, document:
- The task it supports and the decisions or actions that may rely on its output.
- Who can use it, approve its use, change its configuration, and access its data.
- What information enters and leaves the system, including prompts, feedback, logs, and information sent to external services.
- Which models, datasets, software components, hardware, suppliers, and service providers it depends on.
- What could happen if the output is wrong, manipulated, unavailable, or exposed.
- Where human judgment, escalation, or a stop decision is required.
The joint Guidelines for Secure AI System Development (November 2023) frames AI security as a core lifecycle concern, not a one-time gate. Its scope is machine-learning applications broadly; it is not a defense-only deployment manual. The U.S. Department of Defense’s five AI principles—responsible, equitable, traceable, reliable, and governable—also emphasize explicit intended uses and lifecycle testing. Neither source answers legal questions about weapon autonomy or establishes the obligations for a particular mission; those require current, authoritative review for the specific system and context.
Understand the attack surfaces, not just the model
AI can inherit ordinary cyber weaknesses while adding risks associated with data, model behavior, prompts, and training or feedback workflows. The joint secure-development guidance describes adversarial machine learning as exploitation of vulnerabilities in machine-learning components, including hardware, software, workflows, and supply chains. Depending on the system, attacks may degrade predictions, enable unauthorized actions, or expose sensitive information.
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| Risk area | What an attacker or failure may affect | What to examine |
|---|---|---|
| Input manipulation or evasion | Whether a model classifies or predicts correctly when its inputs are deliberately altered or fall outside expected conditions. | Input sources, validation and filtering, operating conditions, and behavior on unusual or adversarial examples. |
| Training or feedback-data poisoning | Whether maliciously modified data can degrade performance, introduce bias, or produce unintended responses. | Data origin, labeling, access, integrity checks, update paths, and any upstream processing or collection. |
| Prompt injection and misuse | Whether instructions embedded in content or user actions can steer a generative system beyond its intended task or permissions. | Prompt and retrieval workflows, tool access, user privileges, and the actions a model can trigger. |
| Privacy and information exposure | Whether sensitive information can be inferred, extracted, or disclosed through model interactions or connected services. | Information sent to the model, retention and logging, access controls, and outputs available to users or external providers. |
| Software, hardware, workflow, or supplier compromise | Whether a dependency or process can undermine the system even if the model itself appears to work as expected. | Component provenance, supplier visibility, build and update processes, service dependencies, and operational access. |
NIST AI 100-2 E2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (March 2025), organizes threats and terminology across predictive and generative AI, including evasion, poisoning, privacy, and misuse attacks. Threats and mitigations vary by system and lifecycle stage; no single defense eliminates all attack paths.
Secure data, models, and external dependencies
Data quality and provenance are security concerns as well as performance concerns. The DoD-hosted Artificial Intelligence and Machine Learning Supply Chain Risks and Mitigations (March 2026) explains that low-quality or biased data can reduce robustness and lead to incorrect classifications or predictions. It also describes how poisoned data can degrade performance, create bias, or cause unintended or malicious responses—and notes that upstream compromise can be difficult to detect, particularly at scale.
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Build checks into the full data path, from collection through labeling, storage, use, and any later update or retraining. Depending on the mission and system, checks may include documenting where data came from, limiting who can change it, reviewing labels and quality, preserving integrity records, and examining how feedback is incorporated. These are controls to consider and tailor, not a universal checklist that proves data is trustworthy.
Apply the same discipline to models and other dependencies. Record what components and services the system relies on, assess what is known about their origin and maintenance, and decide how to respond if a supplier, dataset, model, or service changes or becomes unavailable. NIST SP 800-161 Rev. 1, Cybersecurity Supply Chain Risk Management Practices for Systems and Organizations (published May 2022; updated November 1, 2024), provides a general framework for supply-chain strategy, plans, and risk assessments. Applying that framework to external AI models, datasets, software, and service providers is a practical application of its broad guidance, not AI-specific wording from the NIST publication abstract.
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Test the system against its stated use and plausible attacks
Test evidence should match the intended mission and operating conditions. A model that performs acceptably on ordinary examples may still fail on altered inputs, corrupted data, unexpected context, or a workflow it was not designed to handle. Use technical testing and human-factors evaluation, and document the conditions tested, limitations found, and risks that remain.
- Check performance on representative operating conditions and relevant edge cases.
- Probe plausible adversarial inputs, misuse, and attempts to extract or expose sensitive information, as applicable to the system.
- Review data and model update processes, including how an unexpected change would be detected and investigated.
- Test the surrounding workflow, permissions, dependencies, and interfaces—not only the model’s outputs in isolation.
- Evaluate whether users understand uncertainty, limitations, escalation routes, and the consequences of relying on an output.
- Record findings and retest after material changes to the model, data, software, supplier, or intended use.
The DoD’s published AI principles call for lifecycle testing and assurance, and its June 2021 Joint AI Center briefing transcript records discussion of red-team and machine-learning red-team testing, including whether tools could be misused and whether external data should be vetted for poisoning. That transcript is a historical discussion, not a binding present-day requirement. The cited sources support testing and assurance as considerations, but do not prescribe one universal protocol or guarantee that a particular test will find every vulnerability.
Keep people responsible for context-aware decisions
Human oversight is useful only when people have the preparation, information, authority, and time to act on it. The DoD account of measures endorsed for global militaries (November 2023) calls for training personnel who use or approve military AI so they understand capability limits, make context-informed judgments, and mitigate automation bias—the tendency to rely too readily on an automated recommendation.
Set out which roles may use, approve, override, escalate, or suspend a capability. Train those roles on known limitations, uncertainty, indicators of unexpected behavior, and procedures for handling suspect outputs. Keep records sufficient to trace relevant decisions and changes, while controlling access to sensitive information. Responsibility for a decision should remain clear even when an AI system contributes to it.
Plan how to detect, contain, and disengage
Security continues after deployment. Establish how the organization will notice behavior that departs from the intended use, investigate it, limit its effects, and decide whether to pause or deactivate the capability. Tailor monitoring to the system and mission; possible signals include changes in inputs or outputs, unexpected actions, data or model updates, access anomalies, and loss of a critical dependency. Define who can take action and how relevant records will be preserved.
The DoD’s five AI principles describe governability as including the ability to detect unintended consequences and disengage or deactivate systems that demonstrate unintended behavior. Its published wording says the department will design and engineer AI capabilities to fulfill their intended functions while retaining that ability. This is a principle to implement through system-specific design and operating procedures, not evidence that every deployed system has a tested or effective shutdown mechanism.
Compare acquisition options on the same mission-relevant basis
When comparing candidate systems or acquisition approaches, use consistent questions rather than relying on a general security label or a single performance result. The dimensions below are supported by the cited lifecycle, adversarial-ML, supply-chain, DoD principles, and military personnel guidance; those sources do not rank products or set universal weights.
| Comparison dimension | Question to ask |
|---|---|
| Intended use and error consequences | Are the permitted task, users, decision role, and consequences of an incorrect output explicit? |
| Data provenance and poisoning exposure | Can the source, quality, labeling, integrity, and update path of important data be assessed? |
| Attack surface and dependency visibility | Are the model’s interfaces, connected tools, software, hardware, suppliers, and external services sufficiently visible to assess? |
| Robustness and assurance | What was tested under representative and plausible adversarial conditions, and what limitations remain? |
| Privacy and information exposure | What sensitive information can enter, persist in, or be returned from the system and its connected services? |
| Traceability and audit records | Can relevant inputs, outputs, changes, and approvals be examined when investigating an incident or decision? |
| Human oversight | Are users and approvers trained, and are responsibility, escalation, and automation-bias controls clear? |
| Updates and supplier support | Can changes to components or services be assessed, and is there a plan for supplier or dependency disruption? |
| Containment and disengagement | Can the organization detect unintended behavior and restrict, disengage, or deactivate the capability when needed? |
These comparisons support a reasoned acquisition decision; they do not establish that one system is secure for every mission. Evidence should be specific to the candidate system, its actual configuration, its dependencies, and its intended operating conditions.
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