Fail closed by default
When critical context is missing, actions are intended to stop rather than proceed.
Security and Trust
Security and trust for controlled AI operations.
LandmarkSignal is designed around explicit trust boundaries, auditable pathways, and deployment flexibility for high-assurance enterprise environments.
Security posture
Foundational control principles
Least privilege pathways
Access boundaries are designed to remain explicit for users, agents, and services.
Tenant isolation model
Control and data boundaries are designed to reduce cross-tenant exposure.
Deployment model
Designed to support enterprise deployment options
LandmarkSignal is designed to support self-hosted, private cloud, and hybrid deployment patterns depending on mission and regulatory context.
Hardening profiles and packaging can follow roadmap sequencing by customer environment.
Data handling
Controlled data flow and retention boundaries
Data paths are intended to follow governed boundaries across request intake, policy checks, model routing, tool access, and evidence capture.
Encryption in transit and at rest are designed as baseline expectations, with retention behavior intended to be configurable by deployment profile.
Trust principles
Operating model for high-trust AI
LandmarkSignal is built to support auditable controls across runtime decisions, policy enforcement, and investigation workflows.
It is designed to integrate with enterprise governance processes, risk review, and assurance reporting patterns.
Compliance roadmap
Planned assurance alignment
Certification alignment and framework-specific controls are intended to be developed through roadmap phases and scoped per environment.
No current certification claim is implied on this page; assurance artifacts are expected to follow implementation maturity.
Call to action
Ready to review LandmarkSignal security in depth?
Request a briefing to walk through representative controls, deployment patterns, and trust-boundary design.