Navigate AI data retention requirements with this practical guide. Covers training data, model outputs, logs, and compliance with PDPA and other regulations.
Extend threat modeling methodology to AI systems. STRIDE-AI framework, threat categories, and AI-specific risk assessment.
Systematic methodology for auditing AI vendor security. Includes assessment framework, comprehensive checklist, and common findings.
Extend data classification for AI systems. Policy template for AI data classification, handling rules, and guidance on training data and outputs.
Design appropriate access controls for AI systems. RACI for access management, implementation guide, and guidance on data, model, and output access.
Comprehensive AI security testing methodology covering prompt injection, data leakage, model attacks, and integration vulnerabilities.
Practical defense strategies against prompt injection attacks. Covers system hardening, input validation, privilege separation, and detection mechanisms.
Understand prompt injection attacks on AI systems. Learn how they work, why traditional security fails, and what the risk means for your organization.
Demystify security certifications for AI vendors. Understand what SOC 2, ISO 27001, and other certifications actually prove about vendor security.
50 essential security questions for AI vendor evaluation across data handling, security controls, compliance, and AI-specific concerns. Includes red flag answer indicators.
Complete due diligence methodology for assessing AI vendor security. Includes documentation requirements, evaluation criteria, red flags, and decision frameworks.
Comprehensive guide to protecting student data in AI systems. Covers EdTech evaluation, consent frameworks, and school-specific security controls.
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