ISACA Advanced in AI Security Management™ (AAISM™)
On this ISACA Advanced in AI Security Management™ (AAISM™) course, the industry’s first AI-specific security management certification, you’ll gain an understanding of A...
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The ISACA Advanced in AI Security Management™ (AAISM™) is an AI-centric security management certification designed to supplement certified security managers with the ability to identify, assess, monitor and mitigate risk associated with enterprise AI solutions.
Experienced IT security professionals who hold CISM® or CISSP certifications are also encouraged to join our Beta Program for this advanced credential.
In just 3 days, you’ll be able to navigate the evolving risks of AI, implement essential controls, and ensure its responsible and effective use across the organisation.
You’ll also:
- Validate your expertise in addressing AI-specific security issues.
- Empower yourself to leverage AI for internal growth and innovation.
- Position your organisation as a leader in AI security training.
- Meet the ANSI standards and be the highest-quality credential on the market.
At the end of this course, you’ll achieve your ISACA Advanced in AI Security Management Certification (AAISM) certification.
- Experienced IT security professionals who hold CISM® or CISSP® certifications
- Those with proven experience in security or advisory roles
- Those with expertise in assessing, implementing and maintaining AI systems
Before attending this course, you should have:
- An active CISM or CISSP certification;
- Proven experience in security or advisory roles;
- Some expertise in assessing, implementing, and maintaining AI systems.
ISACA CISA accredited Senior Trainer for AIEA/ISACA Milan Chapter, with managerial experience in IT Audit and Cybersecurity in leading Italian companies and financial institutions.
The course fee includes, in addition to the course days:
– the course slides,
– a set of exercises in digital format,
The official ISACA Manuals are not included in the course fee, but can be requested additionally during registration: their purchase is not mandatory but highly recommended for passing the exam.
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- Stakeholder Considerations, Industry Frameworks, and Regulatory Requirements
- Organizational Structure and Overall Governance
- Roles and Responsibilities
- Charter and Steering Committee
- Identifying Stakeholder
- Risk Appetite and Tolerance
- Frameworks, Standards, and Regulations
- Selecting appropriate Frameworks
- Business and Use Cases for AI
- Privacy Considerations
- AI-related Strategies, Policies, and Procedures
- AI Strategy
- Consumer v. Enterprise
- Buy vs. Build
- AI Policies
- Responsible Use
- Acceptable Use
- AI Procedures
- Implementation
- Manuals
- Ethic
- AI Asset and Data Life Cycle Management
- AI Asset and Data Inventory
- Inventory management
- Model cards
- Data handling, classification, discovery
- Data Augmentation and Cleaning
- Data Storage
- Data Protection
- Destruction
- AI Security Program Development and Management
- Documented Program Plan
- Security team, roles, responsibilities, and proficiencies
- Alignment to existing info sec
- Use of AI-enabled security tools in the program
- Metrics and management
- KRIs and KPIs for AI use with regard to the security
- Management reporting
- Business Continuity and Incident Response
- Incident detection
- Notification
- Incident classification
- Criticality and severity
- Resiliency
- Business Continuity Plan
- Red-button requirements for compliance
- Incident response playbooks specifically for AI
- Break glass policies/ go no go
- Authority
- RTO RPO – AI perspective
- Disaster recovery
- Testing
- Stakeholder Considerations, Industry Frameworks, and Regulatory Requirements
- AI Risk Assessment, Thresholds, and Treatment
- Impact assessment
- Conformity assessment
- PIAs
- Risk documentation
- Acceptable levels of risk
- Treatment plans
- KRIs and KPIs for AI us
- AI-related Strategies, Policies, and Procedures
- PEN test
- Vulnerability tests
- Red teaming
- AI related vulnerabilities
- Adversarial threats
- Threat intelligence
- AI-enabled threats/Attack chains
- Anomalies
- Threat landscape
- Deep fakes
- Insider threat
- AI agents
- AI Vendor and Supply Chain Management
- Dependencies of software packages and libraries
- Vendor due diligence and contracts
- SLAs
- Vendor usage
- Accountability models
- Provider vs. deployer
- Third, fourth, and fifth parties
- Ownership and intellectual property
- Access controls
- Liability
- Vendor monitoring for risk and change
- AI Security Architecture and Design
- Change management
- SDL
- Secure by design
- Securing infrastructure as code
- Data flows
- Approved base models
- Interconnectivity and interaction with architecture
- AI Life Cycle (e.g., model selection, training, and validation)
- Testing models interconnectivity
- Linkages between models
- Regression
- Model testing
- Progression
- TEVV
- Model accuracy testing and evaluation
- Data Management Controls
- Data collection
- Data control
- Data Poisoning
- BIAS
- Accuracy
- Data position requirements
- Privacy, Ethical, Trust and Safety Controls
- Explainability
- Privacy controls – like right to be forgotten, data subject rights
- Consent
- Transparency
- Decision making
- Fairness
- Ethics
- Automated decision making
- Human in the loop
- Trust and safety – content moderation
- Potential harm
- Environmental impacts
- Data minimization and anonymization
- Security Controls and Monitoring
- Security monitoring metrics
- Selecting the right controls
- Implementing controls
- Self-assessment of controls (CSA)
- Control life cycle
- Continuous monitoring
- KPIs and KRIs for security controls and monitoring
- Technical controls
- Threat controls mapping
- Security awareness training
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