A curated collection of essential ai security & data protection resources, organized by type for easy navigation.
11 resources
6 items
Understand the unique data security challenges of AI systems. Covers data classification, access controls, encryption, vendor practices, and essential controls.
Comprehensive guide to preventing data leakage through AI systems. Covers technical controls like DLP, policy frameworks, shadow AI detection, and incident response.
Comprehensive guide to protecting student data in AI systems. Covers EdTech evaluation, consent frameworks, and school-specific security controls.
Demystify security certifications for AI vendors. Understand what SOC 2, ISO 27001, and other certifications actually prove about vendor security.
Understand prompt injection attacks on AI systems. Learn how they work, why traditional security fails, and what the risk means for your organization.
Practical defense strategies against prompt injection attacks. Covers system hardening, input validation, privilege separation, and detection mechanisms.
4 resources
4 items
Implement comprehensive AI data protection with this 15-point security checklist. Each control includes implementation guidance and success criteria.
Complete due diligence methodology for assessing AI vendor security. Includes documentation requirements, evaluation criteria, red flags, and decision frameworks.
50 essential security questions for AI vendor evaluation across data handling, security controls, compliance, and AI-specific concerns. Includes red flag answer indicators.
Systematic methodology for auditing AI vendor security. Includes assessment framework, comprehensive checklist, and common findings.
Talk to an advisor to get personalized guidance on implementing these frameworks in your organization.
