AI Use Policy for Security Operations Template¶
Document Type: Policy Classification: Internal Version: 1.0 (Template — replace all [PLACEHOLDERS] before use)
1. Purpose¶
This policy establishes requirements for the responsible use of artificial intelligence (AI), machine learning (ML), and large language model (LLM) tools within [ORGANIZATION NAME]'s security operations function. AI tools offer significant productivity benefits but introduce risks including hallucination, prompt injection, data leakage, and over-reliance that can impair security outcomes.
This policy aligns with Nexus SecOps benchmark controls AIM (Nexus SecOps-161–180) and LLM (Nexus SecOps-181–200), and the NIST AI Risk Management Framework 1.0.
2. Scope¶
This policy applies to: - All AI/ML tools deployed in or integrated with the security operations center - LLM-based copilots, assistants, or summarization tools used by security analysts - AI/ML models used for threat detection, anomaly detection, or UEBA - Automated decision-making systems that use AI/ML for security conclusions
This policy does NOT restrict general productivity AI tool use outside security operations. See [General AI Use Policy] for those requirements.
3. Permitted AI Uses in Security Operations¶
The following use cases are PERMITTED with appropriate controls:
| Use Case | Risk Level | Required Controls |
|---|---|---|
| Alert summarization and context generation | Low | Analyst review of output |
| Query writing assistance (SIEM/KQL) | Low-Medium | Analyst validation before execution |
| Investigation documentation drafting | Low | Analyst review before submission |
| Playbook guidance lookup | Low | Human decision required for actions |
| Threat intelligence summarization | Medium | Citation required; verify against authoritative TIP |
| Triage decision assistance | Medium-High | Analyst decision required; LLM is advisory only |
| Incident report drafting | Medium | Human review and approval before distribution |
4. Prohibited AI Uses¶
The following uses are PROHIBITED without explicit written approval from [CISO]:
4.1 Automated containment actions based solely on LLM output. All containment decisions (account disable, host isolation, network block) MUST require human decision-making. LLM recommendations are advisory only.
4.2 Definitive threat attribution using LLM. LLMs MUST NOT be used as the authoritative source for attributing attacks to specific threat actors. Attribution requires human analyst judgment and corroborated intelligence.
4.3 Regulatory compliance determination. Legal compliance questions (e.g., "Is this a GDPR reportable breach?") MUST involve qualified legal or compliance personnel. LLM output on regulatory matters is inadmissible as compliance evidence.
4.4 Sending classified or regulated data to external LLM APIs. Data classified as Restricted or containing PII, PHI, PCI, or trade secrets MUST NOT be submitted to external AI APIs without explicit data processing agreements and [CISO] approval.
4.5 Relying on LLM for current threat intelligence without grounding. Using an ungrounded LLM as a source of current IOCs, TTPs, or threat actor information is PROHIBITED due to training data cutoff and hallucination risks.
5. AI Tool Governance Requirements¶
5.1 Inventory¶
All AI/ML tools used in security operations MUST be registered in the AI Tool Inventory maintained by [Security Architecture / CISO Office]. The inventory MUST include: - Tool name and vendor - Use case and scope - Data processing location (cloud region / on-premises) - Data classification of inputs processed - Legal basis for any personal data processing - Last risk assessment date
5.2 Risk Assessment¶
Before deploying any new AI tool in security operations, a risk assessment (per Nexus SecOps-162) MUST be completed covering: - Accuracy and hallucination risk - Data leakage risk (especially for external API integrations) - Prompt injection vulnerability - Bias and fairness implications - Vendor security posture
5.3 LLM-Specific Requirements¶
Grounding: LLMs used for threat intelligence or factual security guidance MUST use Retrieval-Augmented Generation (RAG) or equivalent grounding mechanism, drawing from authoritative internal knowledge bases. Ungrounded responses to factual security questions are unacceptable for operational use.
Citation: LLM responses to factual questions MUST include citations to source documents. Analysts MUST verify factual claims before acting.
Input filtering: Before submitting data to external LLM APIs, personally identifiable information (PII) and sensitive internal identifiers MUST be masked or pseudonymized. The following categories are PROHIBITED from submission to external APIs without specific approval: - Full employee names and email addresses - Passwords or credentials (any form) - Financial account numbers - Regulated health information - Classified strategic information
Prompt injection defense: LLM deployments MUST implement technical controls to prevent prompt injection via analyzed content (e.g., malicious email content, log entries).
5.4 Human Oversight¶
Per Nexus SecOps-191, analyst decisions based on LLM output MUST follow human oversight requirements:
| Decision Type | Oversight Required |
|---|---|
| Alert summary used for context only | Analyst reading the summary (always present) |
| IOC provided by LLM used for blocking | Verify against authoritative TIP before acting |
| Containment recommended by LLM | Tier 2 analyst decision required; LLM input is advisory |
| Incident report drafted by LLM | Human review and approval before distribution |
| Regulatory notification drafted by LLM | Legal review required before sending |
5.5 Logging and Audit¶
All LLM copilot interactions within security operations MUST be logged, including: - Analyst identity - Prompt (or prompt hash if PII is present) - Response - Timestamp - Any action taken based on the response
Logs MUST be retained for [12 months / per retention policy].
6. Model Performance and Monitoring¶
6.1 AI/ML models used for security detection MUST have documented performance metrics (precision, recall, false positive rate) established at deployment.
6.2 Model performance MUST be monitored continuously. Significant degradation (>20% drop in precision or recall) MUST trigger a review within [5 business days].
6.3 Models MUST be re-evaluated after major organizational changes that may cause model drift (e.g., M&A, large-scale workforce changes, new technology deployments).
6.4 Human override capability MUST be available for all AI-driven security decisions. Analysts MUST always be able to override an AI recommendation without requiring approval.
7. Ethics and Fairness¶
7.1 AI tools used in security operations MUST NOT disproportionately flag individuals based on protected characteristics (race, gender, religion, national origin, etc.).
7.2 Before deployment, AI tools used for behavioral analysis (UEBA, insider threat detection) MUST be tested for demographic bias using representative test data.
7.3 Analysts MUST be trained to recognize and report potential bias in AI tool outputs.
8. Vendor and Third-Party AI¶
8.1 All third-party AI tools integrated into security operations MUST be evaluated under [ORGANIZATION NAME]'s vendor risk assessment process.
8.2 Data Processing Agreements (DPAs) MUST be in place before submitting personal data to any third-party AI vendor.
8.3 AI vendors processing Restricted data MUST demonstrate equivalent security controls to [ORGANIZATION NAME]'s standards.
9. Training and Awareness¶
All security operations personnel who use AI tools MUST complete: - AI literacy training covering: how LLMs work, hallucination risks, prompt injection, appropriate use - Hands-on training for each approved AI tool - Annual refresher training
10. Policy Violation¶
Violations of this policy, particularly sending regulated data to unauthorized AI services or acting on AI output without required human oversight, may result in disciplinary action. Security incidents resulting from AI policy violations MUST be reported and included in post-incident review.
11. Document Control¶
| Field | Value |
|---|---|
| Policy Owner | CISO |
| Approved By | [Name, Title] |
| Approval Date | [Date] |
| Next Review | [Date + 12 months] |
| Version | 1.0 |
| Related Standards | Nexus SecOps-161–200; NIST AI RMF 1.0 |