Stratum
Enterprise AI Control Plane

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Discover every AI system (including shadow AI), measure what AI costs and where approved models could work better, enforce policies automatically, and generate audit-ready evidence.

One platform.

Discovery • Analytics • Enforcement • Compliance

  • Continuously monitor behavior
  • Enforce policies through existing infra
  • Prove compliance with live evidence
  • Track spend and optimize ROI

Trusted by security and compliance teams at enterprise AI leaders

THE CRITICAL GAP

AI is in production. Assurance isn't.

AI is everywhere, but invisible.

Your org runs AI in customer support, underwriting, hiring, marketing, code — plus embedded inside SaaS and copilots. Yet you can't answer basic questions:

  • Which AI systems are live?
  • How are they behaving?
  • Who owns the risk?

MLOps, Monitoring, Dev Tools

Great for engineers. Blind for governance.

Only covers what you build

No Runtime
Assurance

Stratum fills this gap

GRC, Risk, Legal

Great for policy. Blind to runtime.

Only covers what you document

MLOps only covers what you build.

Monitoring tools watch a subset of internal models. They don't cover:

  • ×Vendor LLMs (OpenAI, Anthropic, etc.)
  • ×Third-party AI in SaaS products
  • ×Shadow AI usage by teams

GRC only covers what you document.

Policies, risk registers, and DPIAs are static. They don't:

  • ×See live model behavior
  • ×Trigger real-time controls
  • ×Generate technical evidence regulators will accept for AI systems

Regulators, customers, and your board don't care about decks.

They want to know: "Can you prove your AI is safe, fair, and under control — right now?"

WHAT AI FAILURE LOOKS LIKE

Without an assurance layer

Real scenarios where the lack of runtime assurance led to catastrophic outcomes

Silent drift in underwriting

A credit scoring model slowly shifts against a protected group

What went wrong:

  • ×No fresh evals
  • ×No drift thresholds
  • ×No owner alerts

Impact:

Regulatory breach, reputational damage, retroactive remediation

Root cause:

Monitoring saw it; nobody connected it to policy or accountability

Stratum would have:

Raised alertEnforced policyLogged evidence

Hallucinated advice in customer support

Support chatbot gives incorrect financial or medical advice

What went wrong:

  • ×Logs exist
  • ×But no guardrails or eval checks
  • ×No auto-flagging of high-risk answers

Impact:

Harm, claims, investigations

Root cause:

No runtime checks linked to risk policies

Stratum would have:

Raised alertEnforced policyLogged evidence

Shadow LLM in critical workflow

A team wires an unmanaged GPT-4 integration into onboarding

What went wrong:

  • ×No registration in any system
  • ×No assurance of prompts, data usage, or behavior
  • ×Discovered only during external audit

Impact:

Audit finding, deal loss, remediation cost

Root cause:

No central AI system-of-record

Stratum would have:

Raised alertEnforced policyLogged evidence

Missing evidence at audit time

Regulator asks: "Show us your evaluations, incidents, owners, and monitoring trail for this AI system over the last 12 months"

What went wrong:

  • ×Nobody can reconstruct it
  • ×Without days/weeks of manual forensics

Impact:

Delays, fines risk, credibility hit

Root cause:

No continuous evidence graph

Stratum would have:

Raised alertEnforced policyLogged evidence

The Pattern

1

Model deployed

2

No eval refresh

3

Drift occurs

4

Harm

5

Scramble

THE PLATFORM

One platform to see, govern, and prove what your AI is doing

Five integrated pillars that deliver complete runtime assurance

01

AI System Inventory

A living map of your AI estate

Connect to known AI systems, model registries, API gateways, providers, and identity. Register internal and vendor models with risk classification, owners, purposes. Flag unregistered or high-risk usage based on patterns & integrations.

Key Value:

Everyone finally sees: what exists, who owns it, how critical it is.

Central system-of-record for all AI
Automated discovery through integrations
Risk classification & ownership
Shadow AI pattern detection
02

Runtime Assurance Monitoring

Runtime signals, without rebuilding your stack

Ingest metrics from MLOps/monitoring (drift, latency, error rates, eval results). Pull provider telemetry where available. Run lightweight behavioral probes for black-box AI (sampling outputs for factuality, toxicity, regressions). No GPU infra, no log hoarding.

Key Value:

You get a normalized view of AI behavior across internal and external systems.

Ingest from existing MLOps tools
Provider API telemetry
Black-box behavioral probes
Governance-grade signals
03

Policy Engine & Control Orchestration

Executable policies for AI in production

Codify rules like "High-risk models must pass evals every 30 days" or "If drift > X for Y days → require human review." Stratum evaluates policies against live signals and opens cases, creates tasks, or triggers enforcement via MLOps tools, API gateways, IAM, or ticketing systems.

Important: Stratum does not host or serve your models; it orchestrates decisions through your existing infrastructure.

Key Value:

The gap between "we have a policy" and "the policy actually runs" disappears.

Codified governance rules
Automated policy evaluation
Enforcement through existing infra
Case & task management
04

Evidence Graph & Reporting

Evidence you can show to anyone that matters

Every eval, alert, decision, override, and enforcement is timestamped, attributed to an identity, linked to a model & policy, stored in an immutable evidence graph. One-click generation of AI Act technical documentation inputs, ISO/IEC 42001 artifacts, internal risk committee packs, and customer due-diligence reports.

Key Value:

When someone says "prove it," you already can.

Immutable audit trail
Identity-attributed actions
One-click compliance reports
Continuous evidence collection
05

Intelligence & Analytics

Understand the cost, coverage, and performance of AI across your org

Track AI usage & spend by system, team, and vendor. See where high-risk AI runs without adequate assurance. Benchmark assurance posture over time.

Key Value:

AI governance turns into AI strategy — not just risk.

Cross-org usage analytics
Spend tracking by system/team/vendor
Assurance gap identification
Posture benchmarking over time

Works with your existing stack

Stratum integrates with MLOps tools, cloud providers, API gateways, identity systems, and GRC platforms. No infrastructure rebuild required.

Trusted by the teams building, governing, and regulating enterprise AI

Built by the people bridging infrastructure and compliance for the AI era

Stratum was founded by leaders from global infrastructure, compliance, and risk organizations — the people who've built and audited systems running at enterprise scale.

Finally, a team that understands both the runtime telemetry and governance sides of AI monitoring.

VP of Platform Engineering
Fortune 500 Technology Company

The missing piece between MLOps and audit.

Chief AI Officer
Financial Institution

Security & Assurance-Grade Compliance

Verified against global standards — SOC 2 Type II, GDPR, and EU AI Act readiness.
Every policy, alert, and evidence record is cryptographically signed and auditable.

Founding Expertise

Founded by infrastructure, legal, and governance leaders from:

Stanford University
Latham & Watkins
Boston Consulting Group
Deloitte

Backed by advisors from leading AI governance initiatives and Fortune 500 risk committees.

WHY ACT NOW

AI governance is no longer optional

Whether you're scaling AI or just getting started, runtime assurance is now table stakes

Production AI everywhere

AI is moving faster than controls

GenAI + agents in production, customer-facing + decision-making systems. Risk has moved from theoretical → operational.

Compliance is now mandatory

Regulation is no longer abstract

EU AI Act obligations for high-risk & generative systems. NIST AI RMF, ISO/IEC 42001 being adopted. Procurement and customers already asking: "Show us how you monitor and govern your AI."

Need a dedicated layer

MLOps and GRC won't converge

MLOps = built for engineers; can't be the system-of-record for regulators. GRC = built for auditors; can't see runtime signals. The bridge must be a neutral, runtime assurance layer.

Existing tools are inadequate

Traditional tools can't see inside AI

Firewalls and DLP tools are blind to model behavior, drift, and runtime signals. You need purpose-built AI observability.

Competitive advantage now

The first movers will define the standard

Early adopters of AI assurance will: close big enterprise deals faster, navigate audits cleanly, negotiate better insurance, and avoid "AI incident" headlines.

AI assurance is about to become what SOC 2 became for SaaS

You can wait and scramble, or you can operationalize it now.

Questions every AI leader asks us.

Stratum is the intelligence layer for AI — a runtime assurance platform that continuously monitors and governs AI systems in production. It connects technical telemetry from MLOps and providers with policy and identity data to enforce controls and generate real-time evidence. That's how enterprises prove their AI is safe, compliant, and under control.

MLOps watches how models perform; GRC tracks what policies say. Stratum operates between them — translating live AI behavior into compliance evidence and automated actions. It's the missing runtime governance layer neither side covers.

No. Stratum is a control and assurance plane, not infrastructure. It ingests metrics and metadata from your systems and runs lightweight, privacy-preserving probes for black-box AI when needed. Your models stay where they are; Stratum just ensures they behave as intended.

Stratum integrates with your SSO, expense systems, and network telemetry to discover AI usage — from approved SaaS tools to developers spinning up models in personal accounts. It surfaces who's using what AI, where data flows, and which systems lack governance coverage. This inventory becomes your source of truth for AI risk management.

Yes. Stratum shows you AI spend by team, vendor, and system — plus where high-risk AI runs without adequate controls. You'll see which business units have coverage gaps, which systems are cost-inefficient, and where to focus assurance investments for maximum ROI. It turns governance into strategic intelligence.

When drift, bias, or policy violations are detected, Stratum automatically: ① alerts the right owner, ② records immutable evidence, and ③ can quarantine the model via your MLOps, IAM, or gateway integrations. You get enforcement without disruption.

AI is already regulated (EU AI Act, NIST RMF, ISO 42001) and embedded in core workflows. Boards, customers, and auditors now demand continuous proof of control — not annual checklists. Stratum turns governance from a static document into a live assurance system.

Connect your telemetry and identity sources and see your first assurance report in under 30 days. Customers cut audit prep time by 70%+ and prevent incidents that cost millions in remediation and reputation. It's one of the highest-ROI controls you can implement this year.

Still evaluating AI assurance? See how it works in practice.

Make your AI defensible. Before someone asks you to.

Get a live view of your AI systems, policies, risks, and evidence in under 30 days with Stratum. No infra rebuild. No fake dashboards.

Walk through your AI portfolio with our team, or secure early access with exclusive pricing