Every integration matters
A secure model alone cannot protect an application with exposed secrets, broad permissions or unsafe output handling.
Build AI applications on a stronger foundation
Assess the models, tools, APIs and permissions that shape how your AI application behaves.
Request an assessmentDefined boundariesReview in contextClear next steps
Security across your AI ecosystem
Models
Applications
Agents
Infrastructure
Data
Your application is more than its model.
Test the whole workflow
Find how instructions, context and external tools interact before those behaviors reach users.
Bring security into the application lifecycle
Focused assessment, practical context and clear next steps for your team.
AI DISCOVERY
Map the application around the model.
Trace the models, services, data sources and tools your application depends on. Understand where instructions and sensitive information cross a trust boundary.
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
Know your model dependencies.
Review artifact sources, packages and loading assumptions before they become part of your build. Connect model intake risks to your application and deployment process.
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
Test the behavior you ship.
Go beyond functional checks with adversarial prompts and tool-use scenarios. Get reproducible findings that connect unexpected behavior to the code and controls around it.
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
Review coding-agent boundaries.
Examine how development agents access repositories, execute commands and handle project context. Define permissions and review points around the actions that matter.
EXPLORE THE APPROACH →Security workspace◌ ⋯
Control assessment
Request→Policy→Model→Response
Interaction monitoring
InteractionControlStatus
Retrieved contextInput boundaryReview
Tool invocationOutput policyAllowed
Model responseOutput policyAllowed
Security your engineering team can use
Practical engineering context
Understand the systems, dependencies and information in scope.
Earlier risk discovery
Put technical risks in the context of your application.
Safer runtime boundaries
Use reproducible findings to guide the work ahead.
Clear integration guidance
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 application developers.
Ready to understand your AI risk?
Start with your environment, your priorities and a clearly scoped security assessment.
Book a scoping call