Domain 1 of 4 · Chapter 2 of 3

AI Security Use Cases

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Included in this chapter:

  • Sorting a use case by what its output does
  • What has to be true before AI helps
  • Threat detection: malware, phishing and anomalies
  • Defense: a score inside the transaction
  • Security operations: reading and writing at scale
  • Vulnerability management: the model ranks, it does not find
  • Secure code: the gate stays deterministic
  • Exam patterns: judging fit from the stem

What each application area needs and how it fails

PropertyThreat detectionDefenseSecurity operations
What the output isA finding to investigateAn allow, a step-up challenge, or a blockA draft, a summary, or an answer
Who consumes itAn analyst or a case queueThe access or transaction path itselfThe people running the program
Data it needsLabeled examples, or a clean window of normalA score fast enough to arrive before the decisionThe text itself: tickets, logs, reports, documentation
Good looks likeA queue an analyst can finishFewer challenges for legitimate users, and the rest stoppedTime saved with the source still checkable
The costly errorA miss that nothing reportsA false block that stops legitimate businessA confident answer that is wrong
Deterministic alternativeSignature or exact-match ruleA published policy check such as DMARC alignmentA template, a runbook, or a saved query

Decision tree

Can an exact rule settle it,and does it hold still?YesDeterministic checkcheaper, testable, explains itselfNoIs the data already there,in the shape the job needs?NoNot a use case yetinstrument and collect firstYesMust the answer decidethe request in flight?YesDefensescore inline, softer actionNoWill a person investigatethe output?YesThreat detectiona finding for the queueNoSecurity operationsa draft or answer for the team
Cheat sheet

Cheat sheet

  • Sort an AI security use case by what its output does
  • The deterministic check is always on the option list
  • A rule only wins when the condition is both exact and stable
  • Security labels are a by-product of the controls you already run
  • A behavior profile needs an observation window before it can score anything
  • Language jobs need no labels, which is why they go unmeasured
  • Where the output lands decides which error you can afford
  • A score that arrives after the event cannot gate it
  • Proof and inference are graded differently, even inside one product
  • Give an inline policy a recoverable landing
  • Settle the spoofed sender with DMARC and give the classifier the residue
  • Indicator matching is a lookup, not an AI use case
  • In vulnerability management the model ranks, it does not find
  • Keep the code-security gate deterministic and let the assistant argue
  • Name the ground truth before you buy, or you have no use case
  • A malware classifier generalizes by scoring behavior, not bytes
  • In data loss prevention, prefer the exact pattern where one exists

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References

  1. CompTIA SecAI+ (CY0-001) certification
  2. Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 Whitepaper
  3. What are risk detections? (Microsoft Entra ID Protection)
  4. NIST SP 800-94, Guide to Intrusion Detection and Prevention Systems (IDPS) Whitepaper
  5. NIST SP 800-177 Rev. 1, Trustworthy Email Whitepaper
  6. RFC 7489: Domain-based Message Authentication, Reporting, and Conformance (DMARC) Whitepaper
  7. Risk detection types and levels (Microsoft Entra ID Protection)
  8. NIST CSRC glossary: data loss prevention (CNSSI 4009-2015)
  9. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1 Whitepaper
  10. OWASP Top 10 for Large Language Model Applications (2025) Whitepaper
  11. NIST SP 800-40 Rev. 4, Guide to Enterprise Patch Management Planning Whitepaper
  12. NIST SP 800-218, Secure Software Development Framework (SSDF) Version 1.1 Whitepaper