AI Security Tools and Automation

In this course, join cybersecurity expert Brennan Lodge as he demonstrates how Model Context Protocol (MCP) and retrieval-augmented generation (RAG) can be wired into GRC workflows to automate compliance gap analysis and policy assessments. Learn how to build a working system that scrapes privacy policies, compares them against regulatory frameworks—including CCPA, NIST CSF, SOC 2, and GDPR—and generates professional audit reports in minutes instead of hours. Along the way, discover best practices for safe AI deployment, governance considerations, and common pitfalls to avoid when operationalizing AI security tools. By the end of this course, you’ll be equipped with practical automation blueprints that you can apply in your own security environment. This course is an ideal fit for intermediate-level cybersecurity professionals including GRC analysts, compliance officers, and security engineers.

Learning objectives
Explain how AI-driven tools integrate into security operations workflows and enhance traditional compliance assessment processes.
Identify areas where AI automation delivers the most value in GRC operations, including, policy gap analysis, framework mapping, and audit evidence collection.
Describe the foundational concepts of retrieval-augmented generation (RAG) and Model Context Protocol (MCP), and how they securely connect AI models to enterprise security tools.
Recognize security risks and vulnerabilities associated with AI automation in cybersecurity, such as prompt injection, tool poisoning, and overautomation scenarios.
Design AI-driven automation pipelines that map security outputs to compliance frameworks (NIST CSF, SOC 2, GDPR, and DORA) and automate evidence collection for audits.
Evaluate governance, explainability, and trust controls needed when deploying AI automation in security operations.
Apply security-by-design principles and guardrails when building sustainable AI automations, including input validation, privilege management, and access controls.
Assess the effectiveness of GRCautomation through key performance indicators and compliance metrics, audit trails, and determine when human oversight is essential in automated workflows.