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AI Control-Plane Intelligence

Turn Enterprise Telemetry into Smarter Policy

Use AI across a unified control plane to separate meaningful risk signals from everyday noise — and translate what matters into precise, enforceable policy.

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AI for the Unified Control Plane

From a Flood of Telemetry to Focused Enterprise Control

  • Turn Control-Plane Telemetry into Action

    A unified control plane produces an enormous stream of telemetry from users, agents, applications, devices, data, models, APIs, and infrastructure. Primary uses AI to interpret that activity as a connected whole—surfacing the patterns that indicate compromised access, agent misuse, policy drift, or emerging operational risk.

  • Separate Risk Signals from Everyday Noise

    Most enterprise activity is legitimate, even when it appears unusual in isolation. Primary’s AI establishes context across identities, systems, and workflows so teams can distinguish harmless variation from meaningful risk. A late-night login may be routine; paired with a new device, bulk retrieval, and an external upload, it becomes a signal worth investigating.

  • Convert Risk Findings into Enforceable Policy

    Finding risk is only useful if the organization can act on it. Primary translates correlated telemetry into clear policy recommendations, supported by the events and reasoning behind them. Teams can review, refine, simulate, approve, and deploy controls through guided workflows, natural-language instructions, or advanced rule logic.

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  • Connect Intelligence to Every Enforcement Point

    Apply approved policies through identity, DLP, browser, endpoint, network, cloud, application, and agent controls. Primary can restrict a destination, challenge authentication, reduce an agent’s permissions, require human approval, or block a sensitive transfer—coordinating the response across tools instead of leaving another alert for someone to interpret.

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  • Continuously Improve Policies as Work Changes

    Static policies age quickly as new applications, agents, data flows, and processes appear. Primary compares expected behavior with real activity, identifies controls that are too broad, weak, or outdated, and recommends targeted improvements based on observed risk.

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  • Reason About New and Unfamiliar Risk

    AI reasoning helps Primary evaluate requests and activity that have never appeared in a signature or predefined rule. It can recognize an agent operating outside its assigned purpose, a legitimate user assembling an unusual sequence of actions, or several low-severity events combining into a high-consequence workflow.

From Telemetry to Adaptive Governance

How Primary’s AI Policy Intelligence Works

Primary applies AI and machine learning to the telemetry generated across the complete North–South–East–West control plane.

When a user, application, or AI agent takes an action, Primary gathers the relevant context from identity platforms, devices, browsers, applications, data systems, APIs, models, networks, and security infrastructure.

AI then correlates those signals across time and systems. Instead of treating each event as a separate alert, it reconstructs the workflow: who initiated it, what data was involved, which agent or application acted, where the information moved, and what downstream actions followed.

The combined activity is evaluated against expected behavior, data sensitivity, business context, known threats, and risk tolerance.

When meaningful risk emerges, Primary proposes a policy tailored to the pattern—expressed through visual controls, natural language, or advanced rules for security teams to review.

Before activation, teams simulate the recommendation against historical and live activity to measure protection and operational impact.

Once approved, Primary coordinates enforcement across connected systems: blocking an action, narrowing privileges, requiring authorization, isolating an agent, restricting data movement, or increasing monitoring.

The result is a closed-loop control plane in which telemetry improves detection, detection informs policy, and policy outcomes make future decisions more accurate and precise.

Signals That Demand Attention

Real-World Risk Patterns Hidden Inside Enterprise Noise

Compromised identity: an employee account signs in from a new device, enters an uncommon application, retrieves sensitive records, and sends data to an external destination.

Agent overreach: an approved AI agent completes its task, then invokes an unapproved tool, requests broader permissions, and accesses unrelated data.

Coordinated exfiltration: individually permitted queries accumulate into an unusually large dataset, which is summarized by a model, divided into smaller files, and transferred through multiple channels.

Policy drift: a workflow gradually changes until users and agents rely on exceptions that bypass the intent of an existing control.

Each case may generate ordinary-looking events. AI connects them into a sequence, identifies the risk, and explains why it matters.

Primary then recommends a response designed for that specific pattern—reducing investigation time, avoiding blanket restrictions, and helping teams focus on the few signals that warrant action.

Whether the issue is excessive agent autonomy, compromised access, sensitive-data movement, or control drift, unified intelligence helps enterprises respond with greater speed, precision, and confidence.

Control-Plane Policy Intelligence

Combine Unified Telemetry with AI-Generated Governance

Primary uses control-plane telemetry and AI analytics to find material risk and continuously improve enterprise policy decisions.

With context from identity, DLP, SIEM, endpoint, cloud, browser, agent, and application systems, Primary evaluates whether an action should be allowed, challenged, constrained, reviewed, or blocked based on the full workflow.

This reduces the burden of interpreting thousands of disconnected alerts and maintaining overlapping rules independently across every enforcement product.

Behavioral reasoning and natural-language understanding reveal emerging patterns that fixed signatures and thresholds may not recognize.

Consider a finance employee accessing an approved customer record. The activity appears normal until the account invokes an unfamiliar agent, retrieves records at unusual scale, extracts fields, and sends them to an external tool.

No single action violates policy. Together, the sequence may signal a compromised identity, misconfigured agent, or attempted exfiltration—and supports a targeted response.

Operationalize Governance

Create Better Policies from the Signals That Matter

  • Unify Telemetry into Complete Context

    Connect identity, application, browser, device, data, agent, model, network, API, and infrastructure events so AI can reconstruct the complete sequence behind suspicious or high-risk enterprise activity.

  • Detect Multi-Step Risk Across Systems

    Identify threats that isolated tools miss by analyzing relationships across north–south access and east–west activity, including compromised identities, privilege escalation, agent overreach, coordinated extraction, and policy bypass.

  • Generate Policies with Explainable Evidence

    Translate detected patterns into reviewable policy recommendations. Primary describes the risk, identifies the evidence and affected systems, and proposes actions such as narrowing access, restricting destinations, requiring approval, or increasing verification.

  • Simulate Policy Impact Before Deployment

    Test proposed controls against historical and current activity to estimate both protection and disruption. Teams can see whether a recommendation would stop the risky sequence without unnecessarily interrupting legitimate users, applications, agents, or business workflows.

  • Adapt Policies as Enterprise Risk Evolves

    Refine controls as new tools, data sources, agents, behaviors, and threats appear. AI identifies gaps, redundant rules, and outdated assumptions so governance reflects how the enterprise operates—not how it worked when the policy was written.

  • Keep People Accountable for AI Decisions

    Use AI to correlate evidence, explain risk, and draft policy while retaining human judgment for consequential decisions. Administrators can approve, modify, reject, or automate recommendations according to risk and business impact.