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Full Description
Learn how to audit AI, cloud, automation, and the Secure Software Development Lifecycle (SSDLC) using technical evidence, defensible procedures, and risk-based assurance.
Key Features
Validate controls through technical audit procedures, evidence pipelines, and audit-ready telemetry
Audit AI safeguards against prompt injection, sensitive-data leakage, model drift, and unsafe outputs
Assess AI governance, privacy, impact assessments, disclosures, and legal evidence
Book DescriptionModern audits must evaluate automated, distributed, cloud-based, and AI-enabled systems where a compliant-looking record can still conceal a control failure. Cybersecurity & AI Auditing shows how technical competence shapes evidence interpretation, audit quality, and the risk of false assurance.
Tracing the profession from EDP auditing in 1956 to modern cybersecurity and AI assurance, the book teaches readers to validate evidence quality, test controls, select risk-based samples, reconcile populations, investigate exceptions, and report bounded conclusions.
It applies these methods to cloud identity and logging, SSDLC and CI/CD delivery controls, automated evidence pipelines, and AI safeguards such as prompt-injection defenses, sensitive-data protections, output controls, human review, and model monitoring. Provider examples, including Amazon Bedrock Guardrails and Amazon SageMaker Model Monitor, illustrate evidence-led audit procedures, not universal controls. Readers learn to connect technical tests to defensible findings, residual risk, and practical action.What you will learn
Distinguish strong, weak, and misleading audit evidence
Test whether controls are operating and reducing risk
Choose sampling methods based on risk, data, and population
Audit cloud identity, logging, segmentation, and inheritance
Assess SSDLC, CI/CD, dependency, and release controls
Evaluate AI safeguards, attack paths, and impact assessments
Use automation and telemetry without false assurance
Communicate residual risk, uncertainty, and scope limits
Who this book is forThis book is for IT auditors, cybersecurity auditors, GRC professionals, audit managers, assurance leaders, and security teams supporting cloud, application security, SSDLC, AI governance, and control validation. It also helps regulators, cyber insurers, executives, investors, and oversight stakeholders interpret audit results. Readers should understand basic audit, cybersecurity, risk, governance, and technology concepts; however, deep experience in AI, cloud, or software engineering is not required.
Contents
Table of Contents
Understanding why traditional IT audits break down in modern technology environments
Identifying how auditor competence directly affects audit quality and breach risk
Interpreting technical evidence directly within complex systems
Auditing cloud platforms across AWS, Azure, and Google Cloud environments
Auditing the Secure Software Development Lifecycle (SSDLC)
Applying audit assurance to AI models, pipelines, and automated decisions
Moving beyond point-in-time audits using automation and continuous assurance
Measuring and building real technical audit competence using NICE and SFIA
Executing high-quality audits aligned with global standards and professional judgment
Communicating technology risk clearly to executives, boards, and regulators
Demonstrating audit value by linking competence to measurable risk reduction
