The Guardrails Problem
Generative models are powerful and dangerous. They can generate harmful content, be manipulated to ignore safety rules, and produce confidential information if prompted correctly.
Most organizations deploying generative AI ignore these risks. They deploy the model and hope nothing goes wrong.
This approach fails predictably. Someone jailbreaks the model. It generates offensive content. It leaks confidential information. Your organization faces regulatory investigation and reputation damage.
Three Layers of Guardrails
Layer 1: Input Filtering - Prevent harmful prompts from reaching the model.
- Prompt Injection Detection: Identify attempts to override system instructions ("Ignore previous instructions and...")
- Sensitive Data Detection: Block prompts containing confidential information, PII, or classified data
- Jailbreak Pattern Detection: Identify known jailbreak techniques (role-playing, story-telling, hypothetical scenarios)
Layer 2: Output Filtering - Prevent harmful model outputs from reaching users.
- Content Classification: Detect outputs containing violence, abuse, sexual content, hate speech
- Hallucination Detection: Identify outputs that fabricate information or present false claims as fact
- Information Leakage Detection: Identify outputs that contain training data or confidential information
Layer 3: Behavioral Monitoring - Detect suspicious usage patterns.
- Anomaly Detection: User suddenly requesting unusual data or making unusual queries?
- Escalation Detection: User testing boundaries (multiple jailbreak attempts)?
- Sensitivity Tracking: Is a single user repeatedly querying sensitive topics?
Implementation Patterns
Defense in Depth: Use multiple filters. If one fails, others catch problems.
Logging Everything: Every prompt, every output, every filter decision gets logged. When something goes wrong, you can trace exactly what happened.
Rapid Response: If a jailbreak is detected, escalate to human operators. Block the user. Investigate. Patch the system.
Regular Testing: Attempt jailbreaks regularly to verify guardrails are effective. Document what works, what fails, and why.
Sovereign Systems Enable Safe Deployment
Sovereign AI systems let you implement guardrails that cloud-based models cannot.
With cloud AI:
- You cannot modify the model to add safety constraints
- You cannot implement custom input/output filtering integrated with the model
- You cannot access the model's internals for monitoring and debugging
- You're dependent on the vendor's (usually inadequate) safety measures
With sovereign AI:
- You own the model and can add custom safety layers
- You control input/output processing before it reaches users
- You can monitor model behavior and detect anomalies
- You're responsible for safety, which forces you to take it seriously
Organizations deploying generative AI on sensitive data should deploy sovereign models with comprehensive guardrails, not cloud-based models with hope.
Deploy generative AI safely. We help organizations build comprehensive guardrails, safety testing, and incident response for generative AI deployments. Schedule a generative AI safety assessment →