A changing attack surface
AI introduces new dependencies, data flows and behaviors that traditional reviews can overlook.
Take control of your AI security risk
Understand AI exposure, validate your controls, and turn findings into a clear security plan.
Request an assessmentDefined boundariesReview in contextClear next steps
Security across your AI ecosystem
Models
Applications
Agents
Infrastructure
Data
AI security needs a clearer view.
Visibility before assurance
You need a clear view of the systems in use and the controls protecting them.
A clear path through four core AI security questions
Focused assessment, practical context and clear next steps for your team.
AI DISCOVERY
Know what you need to protect.
Map AI applications, model dependencies, connected services and the sensitive information moving between them. Build a useful starting point for assessment and ownership.
EXPLORE THE APPROACH →Security workspace◌ ⋯
System & data flow
Applications12
Models04
Owners08
Inventory by environment
Inventory
67%
67%
AssetEnvironmentReview status
Customer assistantProductionOpen finding
Retrieval pipelineStagingReviewed
Model endpointProductionReviewed
Internal agentStagingOpen finding
AI SUPPLY CHAIN SECURITY
Make model intake a security decision.
Review model sources, packages and integration boundaries before bringing them into your environment. Understand the risks behind a dependency, not only its name.
EXPLORE THE APPROACH →Security workspace◌ ⋯
Model intake review
Model artifactDependency reviewOrigin · integrity · formats
ArtifactSourceReview status
Model weightsRegistryIn review
Tokenizer configRepositoryReviewed
Loading scriptRegistryIn review
Package manifestRepositoryReviewed
AI ATTACK SIMULATION
Find the failures before release.
Test real application behavior against adversarial prompts, unsafe tool calls and information exposure. Turn reproducible findings into a practical remediation plan.
EXPLORE THE APPROACH →Security workspace◌ ⋯
Adversarial test findings
Input scope→Test scenarios→Review findings
↓
Prompt boundaries
Injection / contextData handling
Retrieval / responseTool permissions
Actions / approval
Injection / contextData handling
Retrieval / responseTool permissions
Actions / approval
Assessment evidence
Scenario reproducedReady
Remediation proposedIn review
Retest plannedScheduled
AI RUNTIME SECURITY
Strengthen the controls around AI.
Review identity, data access, output handling and operational visibility. Design defenses around the application and the consequences of its actions.
EXPLORE THE APPROACH →Security workspace◌ ⋯
Control assessment
Request→Policy→Model→Response
Interaction monitoring
InteractionControlStatus
Retrieved contextInput boundaryReview
Tool invocationOutput policyAllowed
Model responseOutput policyAllowed
What your security team gains
A clearer asset picture
Understand the systems, dependencies and information in scope.
Informed model decisions
Put technical risks in the context of your application.
Practical risk validation
Use reproducible findings to guide the work ahead.
A plan for stronger controls
Give your team practical, documented next steps.
“A security assessment should make the next decision clearer. We connect technical findings to your application, your priorities and the work your team can take forward.”
Useful evidence. Clear next steps.Our assessment approach
RESOURCESMake informed
Explore resourcesMake informed
security decisions.
Practical perspectives for security leaders.
Ready to understand your AI risk?
Start with your environment, your priorities and a clearly scoped security assessment.
Book a scoping call