Stratum
Enterprise AI Control Plane

→ discovering ai systems: 247 found

Discover every system. Detect misalignment as it happens. Intervene before outputs cause harm. Generate evidence regulators accept.

One platform.

Discovery • Risk Mitigation • Enforcement • Evidence

  • See every AI system and how outputs flow
  • Detect when behavior misaligns with intent
  • Intervene before outputs propagate
  • Prove control to boards and regulators

Trusted by security, compliance, and engineering teams at AI-forward enterprises

THE DEPLOYMENT GAP

Enterprise AI isn't deploying at scale

Not because models are weak. Because enterprises can't manage risk once AI is embedded in workflows.

AI outputs propagate through approvals, automations, and decisions before anyone verifies they're correct. And existing controls can't keep up.

1

AI Output

Generated suggestion or decision

Propagation

Spreads through workflows and systems

Discovery

Problem found after damage is done

Stochastic behavior can't be fully assured pre-deployment

AI systems are probabilistic. You cannot exhaustively test every possible input/output combination before going live. Edge cases and drift are guaranteed, not theoretical.

Workflow context changes consequences

An output that's acceptable in isolation becomes high-risk when embedded in production workflows:

  • A "suggested approval" becomes an executed payment
  • A resume screening becomes a hiring decision
  • A code suggestion becomes production infrastructure

Outputs reused across systems create emergent risk

AI outputs don't stay contained. They get:

  • ×Fed into other AI systems as context
  • ×Reused across different workflows and teams
  • ×Combined with outputs from other models

The whole becomes riskier than the sum of parts. One misalignment compounds across interconnected systems.

Agents remove the last layer of human control

Agentic systems run continuously without human checkpoints. Each intermediate output feeds into the next state and becomes persistent. Small deviations compound over time and create unacceptable misalignment and risk.

Design-time governance and periodic review no longer scale. The last human buffer is gone.

Which means risk has to be managed where it actually appears: during execution.

WHAT STOPS DEPLOYMENT

Without runtime control: real failures that killed AI programs

These aren't hypotheticals. These are the incidents that make boards say "we can't deploy this."

NON-AGENTICREAL INCIDENT

Silent model drift in lending

JPMorgan Chase (2019): Credit risk model drifted over 6 months, systematically disadvantaging protected groups

What went wrong:

  • ×Drift detection existed but was not connected to compliance policies
  • ×No automated workflow to halt decisions when thresholds crossed
  • ×Impact only discovered during regulatory examination

Impact:

$millions in regulatory fines + 18-month remediation program + all affected decisions reversed

Root cause:

No runtime link between model behavior monitoring and policy enforcement

Runtime control would have:

Detected immediatelyHalted propagationLogged evidence
AGENTICREAL INCIDENT

Agent approval loop compounds error

Financial services firm: Autonomous agent approved vendor payments, each approval fed into risk models as "verified safe"

What went wrong:

  • ×Agent had approval authority up to $50K per transaction
  • ×Made 200+ approvals per day, outputs fed back into fraud detection
  • ×One misclassification cascaded: "approved" status became training signal

Impact:

$12M in fraudulent payments over 3 weeks before manual audit caught the pattern

Root cause:

No runtime verification of agent decisions before they propagated to other systems

Runtime control would have:

Detected immediatelyHalted propagationLogged evidence
AGENTICREAL INCIDENT

Legal research agent cites fake cases

Mata v. Avianca (2023): Lawyer used ChatGPT for legal research, AI hallucinated 6 non-existent court cases

What went wrong:

  • ×Agent generated citations that looked authoritative
  • ×Output went directly into court filing without verification
  • ×Opposing counsel discovered fabricated cases

Impact:

Sanctions, public humiliation, malpractice exposure, AI tools banned from firm

Root cause:

No runtime verification layer between agent output and consequential use

Runtime control would have:

Detected immediatelyHalted propagationLogged evidence
NON-AGENTICREAL INCIDENT

Hiring model perpetuates bias at scale

Amazon (2018): ML recruiting tool trained on 10 years of résumés systematically downgraded women candidates

What went wrong:

  • ×Model learned bias from historical hiring patterns
  • ×Scaled to screen 10,000s of résumés before bias was detected
  • ×By discovery, had already affected real hiring decisions

Impact:

Program shut down, reputational damage, ongoing legal risk from affected candidates

Root cause:

No continuous behavioral monitoring in production context

Runtime control would have:

Detected immediatelyHalted propagationLogged evidence

The Pattern: Discovery comes too late

1

AI deployed

2

Outputs propagate

3

Harm accumulates

4

Discovery (too late)

By the time you find the problem, it's already in production workflows. Runtime control catches it at step 2.

THE PLATFORM

See every system. Detect misalignment. Intervene before harm.

Four capabilities that deliver runtime control

01

Live AI & Workflow Topology Graph

Map every AI system and how outputs flow

Automatically discover AI deployments across your organization — internal models, vendor APIs, shadow systems. Build a live graph of how AI outputs flow between systems, which teams use them, where they connect to business workflows, and how authority accumulates through propagation. This becomes your source-of-truth for what AI exists and where operational risk concentrates.

Key Value:

You see what AI exists, where it runs, and how outputs propagate across your organization.

Automated discovery through integrations
Shadow AI detection via usage patterns
Output propagation mapping
Workflow and organizational context
02

Runtime Behavioral Monitoring - Across Workflows

Observe AI behavior in the context of your business environment, across workflows, continuously

For systems you control: ingest telemetry from your existing monitoring tools and cloud providers. For vendor and black-box AI: run lightweight behavioral probes that sample outputs and check for drift, quality degradation, or misalignment. All signals are enriched with organizational context — so you can detect when AI behavior crosses from acceptable to unacceptable in your specific business context.

Key Value:

You detect when behavior diverges from intent — as it happens, not weeks later.

Telemetry ingestion from existing tools
Lightweight behavioral probes (no GPU infra)
Vendor API metric collection
Organizational context enrichment
03

Proportionate Intervention

Constrain and regenerate outputs before they propagate, without interrupting workflows

When Stratum detects outputs that are risky or misaligned with business objectives, we intervene proportionally. First: constrain the model by injecting tighter context and asking it to regenerate within bounds. If that fails: gate the output and require human review before propagation. If the system is high-risk or repeatedly violates policy: quarantine it. Controls execute through your existing infrastructure — MLOps platforms, API gateways, IAM systems.

Important: Stratum does not host or serve your models. We orchestrate controls through your existing infrastructure.

Key Value:

Outputs that violate policy do not propagate and compound.

Constrain: inject context, regenerate within bounds
Gate: require human review before propagation
Quarantine: block high-risk or repeat violators
Integration with existing enforcement systems
04

Immutable Evidence Trail

Prove control with cryptographic certainty

Every observation, detection, intervention, and override is timestamped, attributed to an identity, and cryptographically signed. When regulators ask "How do you know this system operated as intended?" — you have immutable evidence showing what AI did, in which workflows, under what controls, with full attribution. Generate compliance reports for EU AI Act, ISO 42001, NIST RMF with one click.

Key Value:

When someone asks you to prove control, you have cryptographic evidence.

Cryptographically signed audit trail
Evidence linked to workflows and systems
Identity attribution for all actions
One-click compliance report generation
05

Business Intelligence

Turn governance data into strategic insight

Track AI spend by workflow, team, and vendor. Identify where high-risk AI runs without adequate controls. See where outputs cross organizational boundaries without governance. Understand how risk concentrates and where coverage gaps exist. Turn operational data into visibility on what matters to the business.

Key Value:

AI governance becomes strategic intelligence — not just risk mitigation.

Spend tracking by workflow and team
Coverage gap identification
Risk concentration visibility
Control posture over time

Integrates with your existing stack

Stratum connects to your cloud providers, AI vendors, and internal tools. No infrastructure rebuild required.

Trusted by security, compliance, and engineering teams at AI-forward enterprises

Built by people who understand both infrastructure and risk

Founded by leaders who've built and secured systems at enterprise scale

We realized the problem wasn’t individual models — it was how outputs were reused across workflows. AI Agents kept failing for this reason. Stratum was the only way we could actually stay in control.

VP of Platform Engineering
Fortune 500 Technology Company

“We kept hitting the same wall — AI deployments either failed or introduced risk we couldn’t explain. Once we had runtime visibility and control, we were finally able to move forward.”

Chief AI Officer
Financial Institution

Security & Compliance

SOC 2 Type II, GDPR, EU AI Act readiness.
Every action is cryptographically signed and auditable.

Founding Team

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 THIS IS STRUCTURAL

AI breaks the assumptions enterprise controls were built on

Stochastic systems embedded in operational workflows require a different control model. Enterprises can't policy or process their way back to deterministic governance.

Stochastic behavior is permanent

AI systems do not converge to determinism with scale or maturity. Probabilistic outputs, edge cases, and context-dependent behavior are intrinsic to how these models work — not bugs to be fixed.

Workflow embedding is irreversible

Once AI is embedded in approvals, automations, and decision workflows, outputs become operational inputs. They propagate before verification. Enterprises will not pull AI back into sandboxes.

Static controls break under probabilistic AI systems

Enterprise controls — evals, manual reviews, documented policies — were designed for deterministic software with predictable behavior and defined boundaries.

AI violates every assumption:

  • •Behavior is probabilistic, not repeatable
  • •Risk is contextual, not pre-definable
  • •Impact emerges at runtime, not deployment

You can't slow AI down enough to fit old processes without destroying its value. The control model has to adapt to the system, not the other way around.

Autonomous execution removes buffers

Agents and continuous workflows eliminate the natural pause points where humans could review or intervene. This is not a phase — it is the deployment model enterprises need to compete.

Compositional complexity only increases

AI outputs feed other AI systems, get reused across teams, and influence downstream models. This creates emergent risk that pre-deployment testing cannot anticipate. More AI means more interaction surface.

Runtime control isn't a better version of governance. It's the only model that fits how AI actually behaves in production.

Questions decision-makers ask us

Stratum is a runtime control platform for enterprise AI. We connect to your existing tools and systems to discover every AI deployment, detect when outputs misalign with business intent, intervene proportionally before outputs propagate, and generate immutable evidence of control. Think of it as the layer that makes AI safe to operate at scale.

Performance tools monitor technical metrics — accuracy, drift, latency. Essential for engineering. Compliance frameworks document policies. Essential for audit. Neither provides runtime control. Stratum connects them: translating live behavior into automated intervention and generating technical evidence that regulators accept.

No. Stratum is a control plane, not infrastructure. We ingest telemetry from your systems and run lightweight behavioral probes when needed. Your models stay where they are. We orchestrate controls through your existing MLOps platforms, API gateways, and IAM systems.

We integrate with your SSO, expense systems, and network telemetry. This reveals AI usage across approved tools, vendor APIs, and developer accounts. We surface what AI exists, where it runs, how outputs flow, and which systems lack adequate controls.

Yes. Stratum shows AI spend by team, vendor, and system. You'll see which workflows lack controls, where high-risk AI runs without adequate oversight, and where outputs cross organizational boundaries. Governance data becomes strategic intelligence.

We intervene proportionally. First, we constrain the model and ask it to regenerate within tighter bounds. If that fails, we gate the output and require human review. Controls execute through your existing infrastructure. You get enforcement without disruption.

Agents amplify the problem: outputs become inputs without human review, authority accumulates through sequences of actions, and execution paths diverge faster than humans can follow. Stratum monitors how agent state evolves, detects when actions accumulate unacceptable authority, and intervenes before risk propagates.

AI moved from experiment to operations. It's embedded in production workflows making real decisions. Regulators demand continuous runtime evidence, not annual documentation. The gap between what AI can do and what enterprises can control is widening. You need runtime controls now.

Connect your telemetry sources and identity systems. See your first runtime insights in under 30 days. We integrate with your existing stack — no infrastructure rebuild required. Customers typically cut audit prep time by 70%+ and prevent incidents that cost millions.

EU AI Act, NIST AI RMF, ISO/IEC 42001, SOC 2. We generate compliance reports showing what AI did, in which workflows, under what controls — with cryptographic proof. When regulators ask for evidence, you have it.

Ready to see how runtime control works in practice?

Scale AI with control. Control how AI behaves in real business environments.

Get runtime visibility and control in under 30 days. No infrastructure rebuild.

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