2.6.2. Governing AI Use: Compliance and Usage Policies

💡 First Principle: Governance answers the questions that must be settled before an analyst opens an AI tool — which tools, which data, which uses, and who is accountable for the output — because a data-exposure incident cannot be undone after the prompt is sent.

Legal and regulatory compliance. AI does not create a compliance exemption; it adds one more place data flows. The questions an analyst should recognize:

• Is the data regulated? Log data is not "just technical." Under the EU GDPR, online identifiers such as IP addresses, usernames and device IDs can be personal data. Sending them to an external AI provider is processing by a third party — it needs a lawful basis, a data processing agreement with the provider and, if the provider is outside the EU, a valid cross-border transfer mechanism. The same logic applies to health data (HIPAA), cardholder data (PCI DSS) and any contractual confidentiality obligation.

• Is the AI system itself regulated? The EU AI Act (in force since August 2024, obligations phasing in through 2027–2028) takes a risk-based approach: unacceptable-risk practices such as social scoring are prohibited; high-risk systems (for example AI used in hiring, credit decisions or as a safety component of critical infrastructure) carry conformity, human-oversight and monitoring obligations; limited-risk systems such as chatbots carry transparency duties (users must know they are dealing with AI); minimal-risk systems carry no specific obligations. It applies to AI placed on the EU market or whose output is used in the EU, not only to EU-headquartered companies.

• Can we prove what happened? Evidence handling rules still apply. If an AI tool transformed evidence (deobfuscated a script, summarized a log), the original must be preserved and the AI's role recorded, or the finding may not survive legal scrutiny.

Frameworks that support governance (voluntary, but widely referenced):

| Framework | What it is | Use it to... |

|:----------|:-----------|:-------------|

| NIST AI RMF 1.0 (2023) | Voluntary AI risk management framework with four functions: Govern, Map, Measure, Manage | Structure an organization-wide AI risk program; Govern is the function that establishes policies and accountability |

| NIST AI 600-1 (2024) | Generative AI Profile of the AI RMF | Identify GenAI-specific risks such as confabulation and data privacy |

| OWASP Top 10 for LLM Applications (2025) | Ranked list of LLM application vulnerabilities (LLM01 Prompt Injection ... LLM10 Unbounded Consumption) | Assess and harden an AI-enabled application |

| MITRE ATLAS | ATT&CK-style knowledge base of adversary tactics and techniques against AI systems, with real-world case studies | Threat-model and hunt for attacks on AI systems |

AI usage policies. An AI usage (acceptable use) policy is the internal control that turns those obligations into rules an analyst can follow. A workable SOC AI usage policy states:

• Approved tools — which AI services and deployments are sanctioned, and that unapproved "shadow AI" (personal chatbot accounts, unvetted browser extensions) is prohibited for work data

• Data rules — which data classifications may be entered into which tools (e.g., public and internal data in the enterprise assistant; no regulated data or incident evidence in any consumer tool), with redaction expectations

• Permitted and prohibited uses — e.g., drafting and summarizing permitted; autonomous containment actions or final attribution decisions not permitted without analyst sign-off

• Human review and accountability — AI output must be verified before it is acted on or published, and the human who signs off owns it

• Transparency and records — when AI contributed to a report or finding, that is recorded

• Enforcement — technical backing such as web filtering of unapproved AI domains, DLP rules on prompt uploads, and logging of use of approved tools

⚠️ Exam Trap: Blocking every AI service is not governance — it drives analysts toward personal devices and shadow AI, where the organization has no visibility. The exam-preferred answer is usually a policy that defines approved tools and data rules, backed by technical enforcement, not an outright ban and not an unrestricted free-for-all.

Reflection Question: A proxy report shows 30 SOC staff using a free browser-extension AI to "clean up" ticket notes. What would you put in the AI usage policy to address this, which technical control would enforce it, and why would a total ban on AI tools likely fail?