ISACA's Advanced in AI Security Management (AAISM) Course
- Duration: 16 Hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
9000+ Professionals Enrolled
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Course Overview
The Advanced in AI Security Management (AAISM) Course enables organizations to strengthen the security and resilience of artificial intelligence systems across their lifecycle. The course focuses on identifying AI-specific threats, implementing security controls, and integrating AI security into enterprise risk and governance models. Through structured, scenario-driven learning, participants gain practical expertise in managing AI security risks, ensuring trustworthy AI operations, and supporting secure adoption of AI technologies at scale.
Course Details
- Establish a structured approach to AI security governance
- Identify and manage AI-specific security threats and vulnerabilities
- Integrate AI security into enterprise risk management practices
- Strengthen protection of data, models, and AI-driven processes
- Enable consistent AI security controls across business units
- Support secure and resilient AI adoption across the organization
- Open to cybersecurity, risk, governance, and IT management professionals
- Suitable for experienced practitioners and advanced participants
- Recommended understanding of cybersecurity and enterprise IT environments
- Familiarity with AI, data, or analytics concepts is beneficial
- Ideal for teams responsible for securing or governing AI systems
- Access to digital learning platforms required
- No mandatory prior certification required
- Understand AI security risks across development, deployment, and operations
- Apply AI security governance frameworks and control models
- Assess threats to data, models, infrastructure, and AI pipelines
- Implement security controls for trustworthy and resilient AI systems
- Integrate AI security with enterprise risk and compliance objectives
- Apply AI security management practices to real-world enterprise scenarios
- Support continuous improvement of organizational AI security posture
- AI systems overview and security implications
- AI threat landscape and risk identification
- Data security and integrity for AI environments
- Model security, robustness, and adversarial risks
- Secure AI development and deployment practices
- AI security governance and accountability models
- Monitoring, incident response, and resilience for AI systems
- Enterprise AI security case studies and scenario-based exercises
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