Layered Safety and Accuracy System
Governed AI at the execution layer.
LSAS Stack is an application-layer runtime that sits between apps, agents, APIs, and systems of record to identify risk, enforce evidence thresholds, validate claims, and control what AI is allowed to do before trust is granted.
LSAS is not just about recording what AI did after the fact. It is about measuring risk at runtime, applying deterministic validators where possible, requiring evidence for high-stakes claims, and producing proof of why an output was allowed, constrained, abstained, or escalated.
Not just who can use AI. What AI is allowed to say, do, and rely on - under what conditions, with what evidence, and with what proof.
Deployment options include managed sandbox evaluation, private single-tenant deployment, customer-hosted cloud/VPC deployment, and true self-hosted/on-prem rollout.
The Problem
Prototypes ship fast, but outputs aren't auditable, compliant, or safe enough for production-critical flows.
LSAS in Front
Drop LSAS in front of models, APIs, and healthcare system boundaries to get consistent decisions, governed release, and telemetry by default.
New
Runtime Governance for Regulated AI
Why policy-only AI governance breaks in production
A short executive brief on why governance must move from policy to execution - and what runtime proof looks like in real systems.
Live telemetry & risk analysis
The live telemetry and risk analysis cards on this page are powered by Sandbox traffic flowing through LSAS, so you can see how real decisions, incidents, and domains behave over time. In your own deployment, the same views light up for your apps and tenants so product, security, and compliance teams share a single picture of AI risk.
Incident rate vs baseline
Change vs history
Stable
Riskiest domain right now
Most active rule (last 24h)
HIPAA_MRN
Fired 6 times across demo traffic.
Powered entirely by derived telemetry (decisions, scores, domains, rules) – no raw prompts or payloads.
Release highlights
Now live in this release
A snapshot of the highest-impact capabilities now available across onboarding, governance, and runtime operations.
- Tenant-level AI provider controls with effective runtime policy visibility (provider, model, resilience).
- Retention and deletion governance in onboarding with dry-run lifecycle posture and legal-hold support.
- API key governance posture with rotation intent, stale-key risk visibility, and key hygiene metrics.
- Epic-backed FHIR boundary demo now live for governed healthcare connector search/read flows with runtime evidence.
- Governed Sandbox overrides with explicit approval flow, execution confirmation, and audit telemetry capture.
- Gateway-level resilience and observability primitives, including request and upstream metrics.
Pipeline
How LSAS works
LSAS implements a layered pipeline built for regulated workloads. Deterministic components keep behavior predictable and auditable.
Step
01
Classify Risk
Detect whether the request enters a legal, clinical, financial, security, accessibility, or customer-impacting workflow.
Step
02
Inspect Claims
Identify assertions, recommendations, citations, or actions that require stronger controls.
Step
03
Check Evidence
Evaluate whether claims are grounded in approved sources, retrieval results, structured data, or deterministic checks.
Step
04
Enforce Thresholds
Apply policy-driven evidence requirements, validators, redaction rules, and escalation conditions.
Step
05
Decide at Runtime
Allow, constrain, abstain, or escalate based on risk tier and proof sufficiency.
Step
06
Emit Proof of Findings
Record not just that a decision happened, but why it happened, what evidence supported it, and what failed threshold.
Runtime surface
What LSAS gives you
Deterministic decisions, risk-aware routing, and incident-ready telemetry, as a shared runtime layer in front of models, APIs, and system-of-record boundaries.
Decisioning
Deterministic outcomes
Every call receives a Decision (ALLOW, REDACTED, BLOCKED, ESCALATE_HITL) plus findings and remediation hints.
Risk domains
Domain-specific guardrails
Out-of-the-box domains for HIPAA/PHI, PCI, security, FDA, and accessibility with tunable policy packs, including a HIPAA PHI baseline that is enabled by default in this reference runtime.
Insights
Audit-ready telemetry
Daily rollups and incident views give compliance and security teams a shared source of truth.
Regulated workloads
Built for medtech, fintech, and legal advisory
LSAS focuses on HIPAA/PHI, PCI, security, accessibility, and FDA-adjacent workloads so teams can govern ingress and egress around high-stakes application and system boundaries.
Medtech & Clinical Workflows
Govern high-stakes healthcare and medtech workflows with policy packs, validators, and runtime safeguards around PHI, ingress, and egress. LSAS helps teams apply stronger controls before sensitive output crosses system boundaries.
Designed to support governed healthcare and medtech workflows, not replace your compliance program or clinical judgment.
Fintech & Payment Workflows
Govern payment and financial workflows with PCI-aware validation, secret protection, and runtime safeguards for sensitive prompts, completions, and payloads. LSAS helps reduce risk before high-stakes output becomes operationally trusted.
Designed to support governed payment and financial workflows, not replace internal risk, compliance, or security review.
Legal & Advisory Workflows
Govern high-stakes legal and professional AI workflows with evidence thresholds, claim validation, and escalation controls. Prevent unsupported citations, ungrounded claims, and client-facing hallucinations from surviving into trusted work product.
Designed to govern AI behavior in legal and professional contexts, not to replace legal judgment or professional review.
Why LSAS
More than auditability. Built for layered safety and accuracy.
Many teams hear AI governance and think permissioning, approvals, and audit logs. LSAS goes further.
Many teams hear AI governance and think permissioning, approvals, and audit logs. LSAS goes further. It evaluates runtime risk, inspects claims, checks evidence thresholds, applies deterministic validation where possible, and decides whether the system should allow, constrain, abstain, or escalate.
Audit trails matter, but auditability alone is not enough for high-stakes AI. Trusted AI systems also need structural controls that prevent unsupported output from surviving into regulated, client-facing, or operationally sensitive workflows.
FAQ
Enterprise FAQ
Get detailed answers across product capabilities, model/provider support, healthcare system-boundary governance, implementation approach, and pricing expectations.
Built for deeper evaluation
The full FAQ covers 20+ enterprise-focused questions, including provider compatibility (OpenAI-compatible, Anthropic, and cloud-hosted models such as AWS Bedrock), control boundaries, onboarding milestones for the first 90 days, deployment models, and what teams should expect during implementation.
FAQ Hub
Browse by category or use search to quickly find implementation and governance answers.