Zero Trust AI
A New Paradigm for Secure and Safe AI
Security & Safety in the Age of AI
New Realities
Mistakes, hallucinations, and misalignment events are inherent to Large Language Models. Without the right guardrails, rushing to adopt these technologies introduces significant operational risks, threatening the very reliability your business depends on.
New Responsibilities
Existing laws already hold companies accountable for their outputs, and the inherent properties of LLM-based generative AI introduce new risks into existing operations. Companies have a duty to design, monitor, disclose, and correct agentic AI and its outputs, with the governance and operating framework owned at the CxO level.
Are you Zero Trust AI Ready?
Build Resilience and Reduce Risk in Your AI Deployments with Our 5-Step Approach
Supply Chain & Data Trust Audit
Know exactly what goes in.
Models, training data, retrieval sources, plugins and third-party APIs shape how an AI system behaves. Mapping and reviewing these inputs can help you see where untrusted or unverified material may enter.
Zero Trust Architecture Review
VALIDATE TRUST BOUNDARIES
A review of how models, agents and tools connect can show where trust boundaries, permissions and shutoff controls may need strengthening. This can help reduce the impact if one component fails.
Output Assurance & Controls
VERIFY BEFORE TRUST
Controls such as grounding, validation, confidence thresholds and human review can help catch errors between an AI output and a business action. The right mix depends on the risk of each use case.
Alignment & Behavioural Testing
TEST UNDER PRESSURE
Red-teaming and structured testing explore jailbreaks, prompt injection and agent overreach. The results can help you understand how your AI responds when someone tries to misuse it.
AI Governance & Runtime Monitoring
POST DEPLOYMENT SAFETY
Defined ownership, policy, regulatory mapping and ongoing monitoring can help controls stay relevant as models, data and threats change.
