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I can’t substantiate a first-person account of finding and fixing six specific gaps without the tool’s test records: the tool, test cases, observed misses, changes, and retest results are not established here. What can be stated is how to investigate the question rigorously: define what the AI system does, test the threats relevant to it, record what the controls actually detect, and retest after changes. Frameworks can help organize that work, but they do not certify a product or prove its detections work.
What counts as a detection gap?
A detection gap is a mismatch between a security control’s expected behavior and its observed behavior in a defined test. To call a result a gap, specify the attack scenario, the signal the tool should produce, and what happened instead. A missed alert is not automatically evidence of a product-wide failure: it may reflect the tool’s scope, configuration, data available to it, or a test outside its stated coverage.
Start by describing the system the tool is meant to protect. A predictive model, a generative model, and an application that combines a model with retrieval, tools, or other services expose different surfaces. NIST’s AI 100-2 E2025, published in March 2025, covers adversarial machine learning across predictive and generative AI. Its scope includes attack terminology, taxonomy, lifecycle and attacker context, challenges, and mitigations; it discusses attack families including evasion, poisoning, privacy, and misuse.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsUse frameworks to scope tests, not to certify coverage
MITRE 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 counts describe the framework’s contents, not the frequency of attacks or the detection performance of any tool. MITRE says ATLAS is based on empirical evidence from real-world attack observations and realistic demonstrations by AI red teams and security groups. See the MITRE ATLAS page for its current content.
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MITRE Arsenal is an example of an attack-emulation resource: MITRE describes it as implementing ATLAS techniques to help practitioners emulate attacks against systems containing machine learning. Its existence does not show that a particular product detects those attacks, and it should not be presented as a test method unless it was actually used. See MITRE’s Arsenal announcement.
Use a framework as a map for selecting relevant scenarios. A technique listed in a framework is not automatically applicable to every AI system, and a mapped technique is not proof that testing was complete. NIST characterizes its guidance as voluntary and says it plans annual updates, so confirm the applicable report version and framework content when setting or revising a test plan. The NIST announcement describes the report and update plan.
Build a reproducible detection test
- Set the boundary. Record the model and application components in scope, the tool configuration, relevant data flows, and the environment in which you will test. State what is excluded.
- Choose scenarios that fit. Select threats relevant to those components and the system’s lifecycle. Use a framework to organize candidate techniques, then document why each selected scenario applies. Do not treat the framework as a complete checklist.
- Define the expected signal before running the test. Specify what should be logged, blocked, flagged, or escalated, where that signal should appear, and what evidence would count as a miss. Distinguish prevention from detection: an attack that is blocked and logged differs from one that succeeds without a signal.
- Run controlled tests and preserve evidence. Record the scenario, date, configuration, inputs, system response, alerts or logs, and relevant timing. Keep enough detail for another tester to repeat the case safely and compare results.
- Change one thing at a time where practical. Tie each remediation to the observed failure. A mitigation may reduce one risk while leaving other scenarios or trade-offs unresolved; avoid claiming it closes a whole attack family based on a single test.
- Retest the original case and check for side effects. Confirm whether the expected signal now appears under the same conditions. Also inspect false positives and any operational impact, and record scenarios that remain untested.
Report the six gaps without overstating the evidence
A defensible account of six fixes needs author-specific evidence for every row. The available sources do not identify the tool, the six scenarios, the changes, or any before-and-after results, so those details cannot be supplied as established facts. When publishing an actual test account, use a table like this and replace each field only with recorded results:
| Scenario | Expected signal | Observed miss | Fix made | Retest result |
|---|---|---|---|---|
| Gap 1 — report the tested scenario | State the signal defined before testing | Describe the observed behavior | Describe the change actually made | Report the retest evidence and conditions |
| Gap 2 — report the tested scenario | State the signal defined before testing | Describe the observed behavior | Describe the change actually made | Report the retest evidence and conditions |
| Gap 3 — report the tested scenario | State the signal defined before testing | Describe the observed behavior | Describe the change actually made | Report the retest evidence and conditions |
| Gap 4 — report the tested scenario | State the signal defined before testing | Describe the observed behavior | Describe the change actually made | Report the retest evidence and conditions |
| Gap 5 — report the tested scenario | State the signal defined before testing | Describe the observed behavior | Describe the change actually made | Report the retest evidence and conditions |
| Gap 6 — report the tested scenario | State the signal defined before testing | Describe the observed behavior | Describe the change actually made | Report the retest evidence and conditions |
Label results with their test conditions rather than generalizing from them. A successful retest establishes an outcome for that scenario and configuration; it does not establish complete coverage, eliminate false positives, or demonstrate effectiveness against every related technique.
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Keep coverage current as systems and threats change
Revisit the test plan when the model, surrounding application, data sources, permissions, or security configuration changes, and when relevant framework guidance is updated. Because ATLAS is living and NIST says it plans annual report updates, check the sources themselves rather than treating a saved list of techniques as permanently current. For each review, retain the scenarios, expected signals, results, fixes, and unresolved limitations so the next review can target changed assumptions instead of repeating claims without evidence.
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