AI risk and governance differ from traditional IT risk because the model can generate actions and content that look plausible while being wrong. Input prompts and retrieved context become part of the attack surface. Output quality and latency directly affect analyst decisions and case timelines.

This course frames AI-assisted Security Operations Center (SOC) work through recognized guidance. NIST AI Risk Management Framework (AI RMF) provides a lifecycle approach for identifying and managing AI risk. CISA Secure by Design emphasizes secure defaults, least privilege, and auditable logging for prompts, tool calls, and data access. MITRE ATLAS catalogs adversary techniques against AI-enabled systems and informs SOC playbooks. OWASP Top 10 for LLMs (2025) highlights risks such as prompt injection and Sensitive Information Disclosure, which this course mitigates with practical controls.

The course connects governance, risk, and compliance (GRC) expectations to SOC and engineering practice. It pairs policy with architecture, defines evaluation gates before deployment, and produces evidence that supports audits. The result is AI that improves analyst throughput while preserving confidentiality, integrity, and accountability.

Course Objectives

By the end of this course, you will be able to:

• Translate SOC use cases into safe AI workflows.
• Identify and mitigate the top AI attack and failure modes relevant to SOC.
• Select secure integration patterns for AI in security tooling.
• Apply platform examples to implement triage, investigation, and hunt tasks.
• Validate AI outputs with test cases and red team checks.
• Measure performance and cost with SOC metrics.
• Map deployments to recognized frameworks.