Policy engine
Evaluates runtime requests against tenant policy controls.
LandmarkSignal Shield
Enforcement and controls for AI actions.
Shield is designed to evaluate intent before execution, enforce policy boundaries, and constrain unsafe AI pathways across models, agents, and tools.
What it does
What Shield controls
- Policy decisions across users, agents, and workflows
- Tool and data access boundaries by role and purpose
- Model routing constraints for sensitive operations
- Guard conditions before outbound high-risk actions
Where it sits
Architecture position in the AI stack
Request intent
→User or agent action request enters runtime
Shield policy layer
→Identity, purpose, and access evaluation
Approved pathways
Allowed models, tools, and data scopes
Core components
Enforcement modules
Access gateway
Applies boundary checks for tool, API, and data endpoints.
Model router
Routes requests to approved model classes by trust level.
Threats addressed
Prevent uncontrolled AI action paths
- Unauthorized access to sensitive systems
- Prompt injection leading to unsafe tool calls
- Unapproved model usage in high-trust workflows
Deployment patterns
Designed to support controlled deployment options
Shield is designed to support self-hosted, private cloud, and hybrid deployment models.
Advanced packages can follow roadmap sequencing based on deployment requirements.
What it is not
Shield is not full IAM or enterprise DLP
Shield focuses on AI-runtime policy enforcement and action controls.
It is not intended to replace complete IAM suites or full enterprise DLP stacks.
Use cases and audience
Who uses Shield
Security architecture and AI platform teams use Shield to define enforceable guardrails for production AI workflows.
Example workflow: evaluate request intent, apply policy checks, and return an explicit allow or deny outcome with context.
Call to action