AI Security Checklist for SOC Analysts

Why AI Changes the SOC Analyst's Job

The SOC analyst's job has always been to monitor systems for anomalies, investigate incidents, and contain threats. AI doesn't change that mission — but it introduces a new category of system to protect and a new class of attack to detect.

When your organization deploys an LLM-powered assistant, an ML-based fraud detector, or an AI-driven recommendation engine, each of those systems becomes part of the attack surface. They can be manipulated, abused for data exfiltration, or fed poisoned inputs to influence decisions. None of your existing security tooling was built to catch this.

This checklist covers the three areas where SOC analysts need to expand their coverage: monitoring AI systems, detecting AI-specific threats, and responding to AI incidents.

Part 1: Monitoring AI Systems

Most AI systems produce logs — but few organizations have configured their SIEM to parse and alert on them. The first step is making AI system telemetry visible in your existing detection stack.

• Inventory all AI systems in production. Include internal models, third-party APIs (OpenAI, Anthropic, Cohere, etc.), AI-powered SaaS tools, and any vendor products with "AI" in the feature list. Unseen systems cannot be monitored.
• Ingest API call logs from LLM providers. Most providers expose logs of API calls with metadata: model used, token counts, user/org ID, and timestamps. Pull these into your SIEM. Baseline normal usage patterns — both volume and content category — before you try to detect anomalies.
• Monitor for abnormal token consumption. A user who suddenly consumes 100× their usual tokens may be exfiltrating data via prompt injection or probing model behavior. Set threshold alerts. Spike + off-hours is a high-confidence indicator.
• Log model access and authentication events. Track which service accounts and users have API keys. Alert on new API key creation, key rotation failures, and access from unexpected IP ranges or countries.
• Capture inference request/response metadata. You don't need full content for detection (and may not be able to for compliance reasons). But you do need: request size, response size, latency, error codes, and user identity. These are your behavioral fingerprints.
• Track model version changes. A model update can change behavior in ways that affect security. Log when models are updated in production and compare behavioral baselines pre/post update. Treat model updates like software deployments — they warrant a validation pass.
• Monitor data pipelines feeding AI systems. For ML models that ingest live data (recommendation systems, anomaly detectors), log what data enters the pipeline. Data poisoning often looks like an ordinary data feed anomaly — it won't alert on the model side.

Part 2: Detecting AI-Specific Threats

Classic threat detection logic — brute force, lateral movement, data exfil via DNS — doesn't cover AI-specific attacks. These require new detection rules tuned to how AI systems are abused.

Prompt Injection

Prompt injection is the most common AI-specific attack in the wild. An attacker crafts input that instructs the model to ignore its system prompt, reveal instructions, or take unintended actions. For LLMs integrated into workflows with tool access (code execution, file access, API calls), a successful injection is effectively a privilege escalation.

• Build a prompt injection detection rule. Look for common injection markers in request content: "ignore previous instructions", "you are now", "disregard your system prompt", "jailbreak", "DAN mode". Log all matches at INFO and alert on any with downstream tool invocations.
• Alert on unexpected tool invocations following user input. If your LLM integration can call external APIs or execute code, alert any time a tool is called in a session that also contains injection-pattern markers. The combination is the indicator.
• Watch for indirect prompt injection. Content from external sources (web pages, uploaded files, emails) processed by an LLM can contain injected instructions. If your AI pipeline ingests external content, flag sessions where the content source is uncontrolled and the model subsequently takes an action.

Model and Data Exfiltration

• Detect model extraction attempts. Model extraction is a systematic API query campaign to reconstruct model behavior. Signs: high volume of queries with slight input variations, queries that deliberately probe edge cases, requests from a single IP or account that exceed 10× the 95th percentile volume.
• Monitor for training data exfiltration via memorization. LLMs sometimes regurgitate training data verbatim — PII, API keys, internal documents. This is not a hypothetical: researchers have extracted real data from production models. Flag responses that contain structural patterns of sensitive data (email addresses, phone numbers, key-value patterns matching API credential formats).
• Track AI API key exposure in code repositories. Developers frequently commit API keys for LLM providers to repos. Add your LLM provider key prefixes to your secret scanning rules. This is your highest-probability key-exposure vector right now.

Adversarial and Evasion Attacks

• Baseline model output distributions for production ML models. Adversarial examples — inputs crafted to cause misclassification — shift the statistical distribution of model outputs. You can't catch them per-query, but you can detect drift in aggregate classification metrics over a rolling window.
• Alert on anomalous model confidence scores. Adversarial inputs often produce unusually high or low confidence scores alongside a wrong prediction. If your model exposes confidence, log it. Clusters of low-confidence decisions in a short time window warrant investigation.

Part 3: Responding to AI Incidents

AI incidents don't fit cleanly into standard runbooks. A compromised API key has a standard response (revoke, rotate, investigate scope). But a prompt injection that caused an AI agent to email an external party is a novel chain of events without an obvious playbook. Here's how to approach it.

• Treat AI incidents as data incidents until proven otherwise. The default assumption for any confirmed prompt injection or model manipulation should be: sensitive data may have been exposed or exfiltrated. Initiate your data breach response protocol and scope down from there — don't scope up from "probably nothing".
• Preserve full conversation logs before revoking access. When an AI session is involved in an incident, capture complete request/response logs before you revoke credentials or terminate sessions. Many organizations lose forensic evidence by revoking first. Snapshot, then revoke.
• Establish a model-kill capability before you need it. If your AI system is actively being abused, you need to be able to disable it without a change request and deployment pipeline. This means a feature flag or killswitch that an on-call analyst can flip. If you don't have this, build it now — an incident is not the time to discover you can't turn off the model.
• Scope what actions the AI system could take during the incident window. For AI agents with tool access, reconstruct the complete action log: every file read, API call, message sent, and decision made. The blast radius of an AI incident is determined by what tools the model had access to, not just what data it saw.
• Notify the AI provider if the incident involves a third-party model. If a prompt injection exploited an LLM API you consume, notify the provider's security team. They may have additional telemetry, may have seen the same attack pattern elsewhere, and need to know for their own threat intelligence.
• Document the attack chain for retrospective and detection improvement. AI incidents are novel enough that your post-incident review should produce a new detection rule, an updated runbook, or a configuration change — not just a closed ticket. The field is moving fast; every incident is a learning opportunity your team can't afford to skip.

Building AI Security Competency on Your Team

The checklist above covers immediate operational priorities — what to monitor and detect today. But sustained AI security capability requires deeper knowledge: understanding adversarial ML, LLM security models, AI governance frameworks, and the threat landscape as it evolves.

Security practitioners who can speak the language of both AI systems and threat operations are rare. That's a career advantage if you're an analyst, and a staffing problem if you're a team lead. Closing the gap requires structured learning, not just reading blog posts.

The CAISF certification program was built for exactly this need. Six modules covering the complete AI security attack surface — adversarial ML, LLM security, data pipeline risks, governance frameworks, and production hardening. Pass the 20-question assessment and earn a verifiable credential you can share with employers.

Start the CAISF Course →