Explore the essential AI Security and Compliance Checklist for evaluating SaaS-based AI/LLM applications. Direct penetration testing is often restricted since companies do not control the infrastructure. This makes critical to evaluate SaaS providers. Ensure robust LLM security and AI security by asking the right questions to your SaaS provider before adoption.
1. General Security and Compliance
1.1 Security Certifications
- Does your service hold security certifications such as SOC 2 Type II, ISO 27001, or comply with the NIST Cybersecurity Framework?
- Can you provide evidence of third-party audits demonstrating compliance with industry standards?
1.2 Data Protection and Privacy
- Do you comply with global data protection regulations like GDPR, CCPA, or HIPAA?
- What is your data retention period for user interactions and logs?
- Can users request data deletion, and what process guarantees complete and verifiable removal?
- Do you anonymize or tokenize customer data before using it for model training or analytics?
- What cryptographic standards are applied for data masking or tokenization?
1.3 Data Storage and Encryption
- Where is customer data physically stored? Do you provide regional data residency options (e.g., EU, US, APAC)?
- Is data encrypted using strong encryption standards both at rest and in transit?
1.4 Transparency in Data Use
- Is customer data used for training AI models, or do you use a RAG (Retrieval-Augmented Generation) framework for querying data in real-time?
- What safeguards are in place to prevent data misuse or exposure during training?
- How do you segment sensitive data from training datasets?
- Do you offer an opt-out mechanism for model training using customer data?
2. AI Model Security
2.1 Adversarial and Injection Attack Defense
- Have you conducted a thorough threat modeling exercise to identify potential attack vectors specific to your AI model?
- What controls are in place to prevent prompt injection or adversarial input attacks?
- Do you perform security testing targeting LLM vulnerabilities?
2.2 Content Filtering and Response Controls
- Are output filters in place to prevent the generation of harmful, abusive, or offensive content (e.g., hate speech, violence)?
- How do you handle potentially sensitive content such as sexually explicit material or inappropriate language?
- Do you leverage third-party content moderation services or pre-existing content filtering models (e.g., OpenAI’s content moderation API)?
- Do you provide customizable guardrails for tailoring AI/LLM/Chatbot responses to align with organizational policies?
- Are responses subjected to human-in-the-loop validation or review processes for certain sensitive applications?
3. Identity, Access, and Integration Security
3.1 Role-Based Access and Authentication
- Does your platform support granular role-based access control (RBAC) for restricting LLM-related actions?
- Is Multi-Factor Authentication (MFA) enforced for accessing LLM interfaces and APIs?
3.2 Secure API Integrations
- Are APIs protected with OAuth 2.0, mutual TLS, or signed JWT tokens?
- What rate limiting and request throttling mechanisms are in place to prevent abuse (e.g., DoS attacks)?
3.3 Privileged Access Management
- Are all privileged actions related to LLM configuration and data access logged and auditable?
4. Monitoring, Logging, and Incident Response
4.1 Logging and Audit Trails
- Do logs capture AI inputs, outputs, and metadata (e.g., user ID, session context)?
- How long are logs retained, and are they encrypted at rest?
4.2 Anomaly Detection
- Do you use behavioral analysis or anomaly detection systems for suspicious AI usage (e.g., unusual query patterns)?
4.3 Incident Response
- Do you have a documented incident response plan specific to AI-related security incidents?
- What is your breach notification SLA, and how quickly are customers informed of data incidents involving AI components?
5. Security Testing and Audits
5.1 Penetration Testing
- Can you provide reports of internal AI/LLM-specific penetration testing focused on AI application risks, including OWASP’s top 10 risks for AI security or LLM vulnerabilities?
5.2 Static and Dynamic Code Analysis
- Do you perform regular code reviews and security testing (e.g., SAST, DAST) for vulnerabilities in your AI models and platform?
- How frequently are automated and manual reviews conducted for AI security vulnerabilities?
