I can’t responsibly claim to have found and fixed six detection gaps in a specific AI security tool: no tool identity, test records, gap descriptions, fixes, or retest results are established here. What can be established is how to investigate the question—define the system and threat scope, test the controls against relevant scenarios, and report only observed outcomes. The result is a practical method for finding detection gaps without mistaking a threat framework for proof that a product works.
What counts as an AI security detection gap?
A detection gap is a tested scenario in which a control fails to produce the signal or response its operators expected. Define the protected system, the attack scenario, the expected signal, and the evidence you would accept before testing. Without those definitions, a missing alert may be a coverage problem, a logging problem, an out-of-scope event, or simply an expectation that the tool never promised to meet.
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Scope matters because AI security is not one model or one stage. NIST’s March 2025 report, AI 100-2 E2025, covers adversarial machine learning in predictive and generative AI, including evasion, poisoning, privacy, and misuse. Select scenarios that match the actual system: its model type, data flows, deployment lifecycle, and surrounding controls. A test of a model endpoint does not by itself establish coverage of training data, connected tools, or downstream workflows.
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How do I test AI security detections?
1. Set the boundary and success criteria
Record which components are in scope—such as model endpoints, retrieval sources, training or fine-tuning pipelines, identity controls, and application logging—and which are not. For every scenario, specify the expected observable behavior: for example, an alert, a blocked request, a logged event, or an escalation to an operator. Detection, prevention, and response are different outcomes; report them separately.
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2. Build scenarios from relevant threats
Use a threat map to find candidate scenarios, not to claim exhaustive coverage. MITRE’s ATLAS is a living knowledge base of adversary tactics and techniques involving AI. Its page reported 16 tactics, 208 techniques, 40 mitigations, and 73 case studies when accessed in October 2026; those are counts of framework content, not attack prevalence or a score for any tool. MITRE says ATLAS draws on real-world attack observations and realistic demonstrations by AI red teams and security groups.
MITRE describes Arsenal as an attack-emulation library implementing ATLAS techniques. Emulation is one way to exercise assumptions, but using a framework or test library does not certify a product or establish that it detects a technique.
3. Run controlled tests and preserve evidence
For each scenario, capture the test conditions, relevant configuration, expected signal, actual behavior, timestamps, and logs or alerts. Keep the test reproducible and distinguish a tool’s detection result from other controls that may have blocked or altered the event. If the result is ambiguous, label it inconclusive rather than a pass.
4. Fix the cause, then retest the same scenario
Map a confirmed miss to the control that should address it, make a documented change, and rerun the same test under comparable conditions. Record whether the expected signal appeared and whether the change introduced false positives or disrupted normal use. A mitigation is meaningful only in relation to the scenario and conditions tested; no single mitigation should be presented as closing every AI security gap. NIST’s report discusses both mitigation methods and their limitations.
How to report six gaps without overstating the result
For an actual review, use one row per verified finding. The six gap names, tool identity, observed misses, changes, and retest outcomes must come from the reviewer’s records; they cannot be inferred from general threat guidance.
| Scenario | Expected signal | Observed miss | Fix made | Retest result |
|---|---|---|---|---|
| Fill from test record | Define before running | Document observed behavior | Document actual change | Report evidence and conditions |
| Fill from test record | Define before running | Document observed behavior | Document actual change | Report evidence and conditions |
| Fill from test record | Define before running | Document observed behavior | Document actual change | Report evidence and conditions |
| Fill from test record | Define before running | Document observed behavior | Document actual change | Report evidence and conditions |
| Fill from test record | Define before running | Document observed behavior | Document actual change | Report evidence and conditions |
| Fill from test record | Define before running | Document observed behavior | Document actual change | Report evidence and conditions |
Do not turn a successful retest into a broad coverage claim. State which scenarios were exercised, what the test observed, what remained out of scope, and any known false-positive or false-negative behavior. A framework mapping can help readers understand the scenario, but it is not evidence that all related techniques were tested.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to keep detection coverage current
Revisit tests when the system changes—such as a model, data source, tool integration, deployment path, or monitoring configuration—or when threat assumptions materially change. Keep the scenario set versioned so teams can compare results across releases. Recheck live resources near the time of a review: ATLAS changes as a living knowledge base, while NIST says it plans annual updates to its adversarial-ML report. NIST describes its guidance as voluntary; neither source guarantees that a particular tool detects a particular attack.
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