One policy plane to rule them all

The multi-provider problem is structural, not accidental

Six months ago it was OpenAI for the core feature. Then someone added Claude for a new workflow. Now Copilot is rolling out org-wide. Teams don't deploy multiple providers out of carelessness—they do it because Claude suits long-context analysis, GPT-4 suits rapid generation, and Copilot suits developer productivity. Each is a legitimate choice. But each provider has its own event schema, identity model, policy surface, and audit capability.

So you're not managing one AI governance problem. You're managing three or four, in isolation, with no shared language and no shared visibility. When an anomaly occurs, your first challenge isn't the investigation—it's figuring out which provider's console to open first, then manually correlating events across systems that were never designed to talk to each other.

Median triage times at most SaaS companies with multi-provider AI are measured in hours, not minutes. Not because the engineers aren't skilled—because the tools aren't built for the problem.

Alert fatigue is a governance failure, not a volume problem

The reflex is to tune alerts more aggressively—raise thresholds, suppress noisy detections. This usually makes things worse. Alert fatigue in AI security is a signal quality problem: legacy SIEM rules built for human behavior don't map onto agent behavior. An agent calling a database API a thousand times isn't necessarily compromised—that's what agents do. But an agent accessing schemas it has never touched, at 3am, producing output that matches exfiltration patterns, is a signal worth acting on immediately.

Telling those apart requires context generic rules don't have: what is this agent's baseline? What is it authorized to access? Lineation is built around agent-native detection—rules that understand baselines and evaluate actions against declared policy.

What enterprise-grade governance requires at your scale

Centralized policy, distributed enforcement

You shouldn't write four versions of the same policy in four provider-specific formats. Lineation's control plane is the single place you define access rules, output constraints, and risk thresholds—for all providers and agents. The engine normalizes provider events into a common schema and logs decisions uniformly.

Agent registry and identity graph

Every deployed agent gets a machine identity with declared scope, owner, and capabilities. Discovery is automatic—agents are catalogued whether or not they were formally provisioned, including the shadow AI a product team deployed via Zapier.

Cross-provider activity lineage

A complete, time-sequenced record of every significant interaction, normalized across providers: prompt, tools called, data accessed, output, and the actor or schedule that initiated the chain. This is what makes the difference between a 20-minute triage and a two-hour one.

Precision-tuned anomaly detection

Detection tuned against agent baselines, not generic user patterns. An agent that has never touched a schema gets a different risk score than one for which that access is routine. The target is below 15% false positives.

SIEM/SOAR integration as first-class output

You're not replacing your security operations stack—you're filling the AI gap in it. Normalized agent events, risk-scored alerts, and incident context are all available in the format your existing tooling expects.

What changes when you deploy Lineation

• Alert queue quality: alerts become agent-behavior-aware and risk-scored before they reach you. The false-positive rate drops.
• Incident triage: start from the normalized lineage record and arrive at a defensible summary in under thirty minutes.
• Policy coverage: a policy plane that covers every connected provider and registered agent—gaps visible in a dashboard, not discovered during incidents.
• Provider expansion: register the agent, declare its scope, connect the provider, inherit the ruleset. From a week of custom work to an afternoon of config.