Enterprise AI Security & Governance - ISACA AAISM AAIA AAIR

Establish a solid AI security foundation by exploring core concepts and certifications, as the course transitions from a slide-heavy introduction to hands-on, interactive security modules.

Explore how organizations secure AI systems by balancing innovation with governance, risk management, and continuous monitoring to guard against data poisoning, prompt manipulation, and adversarial attacks.

Close the AI security gap by moving from reactive to proactive security, embedding governance, risk assessments, access controls, and continuous monitoring into AI initiatives.

Discover the AAISM certificate by ISACA, the first AI security management certification, teaching governance, security, and lifecycle monitoring of AI systems.

Clarify AI security roles—from risk analyst to chief AI security officer—and translate technical risks into business impact, guiding governance and accountable, trustworthy AI.

Define AI governance to design, deploy, monitor, and control AI systems with policies, oversight, and accountability, while emphasizing data governance and incident response for safety and trust.

Identify and assess AI risks across the full lifecycle. Mitigate them with controls like access controls, monitoring, encryptions, validation mechanisms, and anomaly detection.

Emphasize security by design to build AI systems with protection from data collection through retirement. Prioritize data quality, governance, access controls, fairness, transparency, and accountability.

Explore how AI reshapes threat landscape by targeting models, training data, prompts, and behavior. Learn defenses for prompt injection, data poisoning, and model theft with input validation, instruction isolation, monitoring.

Assess advanced AI threats and risks of retrieval poisoning, data leakage, multi-tenant data risks, identity spoofing, and runtime manipulation, then implement defenses like integrity controls and monitoring.

Examine vulnerabilities in AI infrastructure, including shadow AI, non-human identities, supply chain risks, and agentic or coordinated AI threats; emphasize governance, visibility, credential management, and least privilege access.

Explore AI security controls such as input validation, output monitoring, and automated alerts to keep AI systems safe. Enforce least-privilege access, encryption, and continuous monitoring to detect anomalies and threats.

Empower humans as the key safeguard in AI security by validating outputs, overseeing critical decisions, and maintaining accountability through human-in-the-loop oversight and risk-based audit trails.

Build preparedness for AI threats with rapid incident detection, monitoring, analytics, alerts, containment, and guided recovery. Learn AI forensics and structured response to strengthen governance, trust, and resilience.

Learn how AI compliance and regulations shape enterprise security and governance, with GDPR, the EU AI Act, and standards like ISO 42001 and NIST guiding compliance by design.

Establish strong metrics and KPIs aligned with business objectives to measure AI security performance, monitor detection and response times, drift, and integrity, and report through real-time dashboards for leadership visibility.

Define an AI security strategy and a proactive roadmap by establishing visibility through a complete inventory of models, systems, data sources, integrations, and shadow IT, prioritizing risks by business impact.

Navigate the AI security and governance maturity model, starting with asset discovery of models, data sources, APIs, and integrations; advance to risk awareness, implement controls, and monitor toward governance maturity.

Insecure AI has financial and reputational impact, with security incidents causing penalties, losses, and public distrust. Strong AI security enables resilience, protects operations, and strengthens trust and competitive advantage.

Explore how ai security blends cybersecurity and ai expertise to meet rising demand, unlock leadership roles like chief ai security officer, and shape digital trust through continuous learning and certifications.

Address AI security as a foundational requirement for responsible innovation, emphasizing prevention over reaction, proactive preparation, and building trust with customers, regulators, and stakeholders.

Explore bonus materials, checklists, and roadmaps for enterprise ai security and governance, including governance, risk assessment, incident response, continuous monitoring, ethical ai, and security by design.