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Preparing your SOC for AI
AI risk is not a simple extension of traditional IT risk. Models change behavior, which creates new failure modes like prompt injection, insecure output handling, data leakage, and drift. You need controls that watch the interaction pattern, not only the code at rest. In this course, you will tie security frameworks to everyday SOC tasks.
Course Content
AI risk is not a simple extension of traditional IT risk. Models change behavior with data, prompts, and tools at runtime, which creates new failure modes like prompt injection, insecure output handling, data leakage, and drift. You need controls that watch the interaction pattern, not only the code at rest.
You will use NIST AI RMF to structure governance across Govern, Map, Measure, and Manage. CISA Secure by Design guides secure defaults, telemetry by design, and memory safe implementation choices for AI features you embed. MITRE ATLAS provides threat-informed behaviors and test ideas for adversarial use against AI-enabled workflows. The OWASP Top 10 for LLM Applications focuses your controls on LLM-specific risks such as prompt injection and over-reliance.
Throughout the course, you will tie these frameworks to everyday SOC tasks. Detection engineers will translate risks into guardrails and monitors. Incident responders will adapt playbooks for AI-specific events. GRC partners will maintain a defensible control map and evidence trail. The focus stays on measurable improvements to MTTA, MTTR, accuracy, latency budgets, safety rates, and cost.
Course Objectives
By the en of this course, you will be able to:
- Select high-value SOC AI use cases that improve analyst throughput and reduce MTTA and MTTR. Why it matters: Good selection delivers measurable impact and avoids waste.
- Design secure AI architectures for sidecar assistants, summarizers, classifiers or routers, and agents with tools. Why it matters: The pattern drives latency, cost, and data exposure.
- Apply risk and control frameworks to AI in the SOC, including NIST AI RMF, CISA Secure by Design, MITRE ATLAS, and OWASP Top 10 for LLMs. Why it matters: Control coverage prevents avoidable incidents and supports audit.
- Prepare SOC data for AI with minimization, labeling, retention, and access controls. Why it matters: Data quality and governance drive model performance and compliance.
- Instrument and evaluate AI features with offline tests, live telemetry, and red team exercises. Why it matters: Auditable evidence sustains funding and trust.
- Operationalize AI incident response for prompt injection, data leakage, model misuse, and drift. Why it matters: AI introduces new failure modes that must be contained quickly.
- Build a pragmatic adoption roadmap with milestones, budgets, and decision gates. Why it matters: Staged rollout reduces risk and accelerates value.
