Auditing Artificial Intelligence

North Texas Chapter of ISACA

During this course, auditors will learn the skills needed to effectively audit AI.

Auditing Artificial IntelligenceBusiness is constantly evolving. As part of evolution, businesses are turning to AI to improve and perfect their operations. AI is being used in a variety of ways to improve efficiency, save time and decrease costs. AI is constantly advancing and becoming a resource for companies in all industries. Auditors play a key role in ensuring the security, compliance, effectiveness and privacy of AI when used by organizations. During this course, auditors will learn the skills needed to effectively audit AI. Topics such as the fundamentals of AI, challenges and risks introduced by AI, and the key steps auditors should take to audit AI will be addressed. Students will learn about AI governance, AI management, AI policies, and data audits including the assessment of Large Language Models (LLM). By developing the skills necessary to audit AI, students will be able to assist their organizations to ensure the AI strategy being pursued is addressed in a secure, and effective manner.

Dates: August 20, 2026 - August 21, 2026 Time: 9:00 am to 5:00 pm Central Time (1 Hour Break for Lunch)Location: Remote (Zoom) CPE: 16 (2 days) Format:Remote (Zoom)<h4>ISACA Non-Member Pricing: $680 (Early-bird pricing available)</h4><h4>ISACA Member Pricing: $650 (Early-bird pricing available)</h4>FEE Notes:<ul><li>All cancellations must be made prior to August 13, 2025, for a full refund.</li><li>No cancellations will be accepted after August 13, 2025.</li></ul> Instructor:Tanya Baccam, Consultant, Senior SANS Instructor, CISSP, CPA, CITP, CISA, CISM, GPPA, GCIH, OCP DBA Tanya Baccam has extensive experience performing audits and assessments including application reviews, system audits, vulnerability and penetration tests, as well as providing training around application and software development risks. She is skilled in reviewing the security architecture for clients, including assessing firewalls, applications, web sites, network infrastructure, operating systems, routers, and databases. She has conducted multiple network penetration engagements, vulnerability assessments and risk assessments using an arsenal of tools including commercially available and open-source tools. She has developed and reviewed policies and procedures, as well as developing and providing security awareness training. Tanya has conducted, scheduled and managed numerous security assessment engagements. Additionally, she has provided advice and guidance to multiple companies on how to build successful auditing practices. During her career in Information Technology, Tanya has become an expert in network and application security services. She has worked in management, training, and consulting roles. She has vast experience including support of Novell, UNIX, Windows, and Oracle platforms. Tanya is a Senior Certified Instructor and courseware author for SANS (Sysadmin, Audit, Network, Security) where she has developed and delivered training in security auditing, incident handling, hacker exploits, database security and perimeter protection, as well as being an authorized grader for some of the GIAC certifications. She is also a member of ISACA (Information Systems Audit and Control Association).

Course AgendaOutline:I. Introduction • AI Background • AI Terms • Understand machine learning models, AI models, and Gen AI models • Data sources for AI • Deployment strategies • Feature engineering • Types of AI available • LLMOps Framework • AI Lifecycle • AI Bias • Adversarial Training • Trustworthy and Responsible AI Principles II. Understanding AI Prompts/Training • Building prompts • Common AI Prompt Mistakes • Optimizing LLMs III. Managing risk related to AI • AI versus Traditional Software Risks • Positive and negative implications of AI • Risk management techniques • AI standards and policies IV. Challenges for AI Risk Management • Understanding an organization’s AI vision and strategy • Risk measurement • Risk tolerance• Risk prioritization • Harmful bias • Unintended/unpredictable consequences • Security concerns • Understanding the attack surface • Third party risks V. Auditor’s role in Evaluating Efficiency and Bias in AI • Addressing bias in AI • Data quality • AI KPIs • Efficiency • Effectiveness • Business impact • Technical performance • Fairness/compliance • Harmful bias identification • Monitoring and Auditing VI. AI Security • OWASP Top 10 for LLM Applications • Prompt Injection • Sensitive info disclosure • Supply chain • Data and Model poisoning • Output handling • Excessive agency • System prompt leakage • Vector and embedding weaknesses • Misinformation • Unbounded consumption • OWASP API Security Top 10 • AI Vulnerability repositories • AI policies • AI committee • Ethical AI practices • Data protection controls • Validation and Training • Access controls • Audit security checklists for LLM applications and GenAI VII. AI Auditing and Risk Management Frameworks • Govern, Map, Measure, Manage • NIST AI Risk Management Playbook • Controls for mitigating AI risk • Well-architected Machine Learning Design Principles • Well-architected frameworks VIII. ConclusionLaptop Required:Students are required to have a laptop in order to complete the hands-on exercises. The laptop should meet the following specifications for the student to get the most from the exercises:• USB Port• 8 GB RAM or higher• 25 GB available hard drive space• Windows 10 professional or later (Home or similar editions will not have some of the features needed.)• Administrator privileges including the capability to install and run tools, as well as disable anti-virus• VMWare Player should be installed

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