Policy Engine
Evaluates requests against tenant policy, role intent, and workflow guardrails before action is allowed.
LandmarkSignal Runtime
The control plane for enterprise AI operations.
Runtime is designed to govern how AI requests move across identity, policy, models, tools, and data boundaries so teams can support high-trust operations with representative traceability.
What Runtime controls
Governance before execution
- Agent identity and tenant context resolution before execution
- Policy enforcement with explicit allow or deny outcomes
- Tool governance for external APIs, internal systems, and data stores
- Model routing based on sensitivity, task profile, and trust boundaries
- Data access boundaries for knowledge bases, logs, and evidence records
- Audit trails with representative trace metadata for review workflows
Where Runtime sits
Architecture position in the AI stack
Users / Agents / Workflows
→Requests and automation intent
Runtime
→Identity, policy, routing, and boundary controls
Models / Tools / Data
→Approved execution pathways
Audit / Evidence
Representative trace and review artifacts
Core runtime components
Modules designed to support controlled AI execution
Tool Gateway
Mediates access to approved tools and APIs with scoped permissions and decision logging.
Model Router
Routes requests to approved model paths based on policy constraints and workload requirements.
Identity & Tenant Controls
Binds each request to actor identity, tenant scope, and intended purpose context.
Audit Layer
Captures representative traces, decision reasons, and execution metadata for governance review.
Request lifecycle
How Runtime handles each request path
- 1. Request enters Runtime
- 2. Identity and tenant context are resolved
- 3. Policy is evaluated for role, purpose, and data scope
- 4. Tool and model access are mediated through governed pathways
- 5. Result is returned through the approved execution path
- 6. Audit trace is recorded for downstream review
Threats addressed
Reduce high-risk failure modes in enterprise AI workflows
- Prompt injection attempts that try to bypass policy intent
- Data exfiltration risks across tools, models, and outbound channels
- Tool misuse through over-permissioned or ungoverned execution
- Privilege escalation via ambiguous identity or tenant context
- Unsafe model routing for sensitive or mission-critical tasks
- Missing audit evidence during governance or incident review
- Shadow AI visibility gaps across distributed workflows
- RAG data leakage from mis-scoped retrieval boundaries
Deployment patterns
Designed to support constrained enterprise environments
Runtime is designed to support local or self-hosted deployments, private cloud options, and hybrid operating models based on governance requirements.
Air-gapped packaging and deeper environment hardening are intended to follow roadmap sequencing and deployment-specific validation.
What Runtime is not
Runtime is not a standalone model or generic dashboard
Runtime is not an LLM, not just an inference server, and not only an agent framework.
It is not a generic observability dashboard and is not intended to replace IAM, SIEM, or DLP platforms.
Use cases
Workflows teams are built to support with Runtime
- Cybersecurity investigation
- OSINT research
- SOC 2 evidence collection workflows
- Vendor questionnaire review
- Business intelligence monitoring
- Developer and code-review support
- Customer-support automation
- Multi-agent workflow coordination
Call to action
Ready to see LandmarkSignal in action?
Request a focused Runtime briefing to review representative governance flows, deployment options, and adoption sequencing.