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AI Access Security
Secure AI Usage by People
Enable AI adoption across your workforce without sacrificing security or compliance.
Empower your users to adopt AI safely and boldly
Empower safe, scalable AI growth with unified governance that ensures visibility, control, and trust.
Gain continuous, organization‑wide AI visibility to proactively detect, measure, and reduce risk.
Ensure instant audit readiness with complete AI interaction records that streamline reporting and compliance.
AI adoption is outpacing AI governance—and the distance is growing
Nearly half of organizations expect security incidents caused by shadow AI tools in the next year. Yet most lack the ability to detect them. Risks increase as AI evolves from standalone apps based on large language models (LLMs) into embedded apps, autonomous agents, and Model Context Protocol (MCP) servers.
Legacy security was built to secure access to cloud apps, not to inspect AI interactions, govern agent behavior, or provide forensic evidence for boards and regulators.
Harness AI with clarity, confidence, and control
Accelerated AI adoption across the enterprise
Seamlessly scale your AI initiatives, with governance of every layer from day one. Security teams get the confidence to approve AI use, and leadership gets assurance that risk is managed as AI adoption expands. The same visibility, enforcement, and forensic capabilities extend to new AI tools, apps, and agents as you adopt them, growing your AI strategy without creating ungoverned exposure.
Reduced risk exposure from unsanctioned and unmonitored AI
For the first time, give your security teams a complete, continuously updated view of AI activity organization-wide. Risk that was once invisible becomes measurable and manageable. As AI evolves from standalone tools into embedded apps and autonomous agents, visibility scales with it. Risk posture improves even as your AI environment grows more complex.
Defensible audits without manual reconstruction
Produce a complete forensic record of every AI interaction. The record shows who initiated the interaction, what the AI did, what data was involved, and which policies were applied. Report to boards, regulators, and auditors quickly and easily, rather than spending weeks assembling evidence from fragmented logs. As regulatory scrutiny of AI intensifies, make continuous audit readiness a strategic advantage, not an operational burden.
AI Discovery and Inventory
Discover and inventory all AI applications (first-party, third-party, embedded), AI agents (custom and managed), tools, integrations, external services, and MCP servers.
AI Runtime Observability
Get continuous runtime visibility into how AI systems behave when deployed, with behavior-level telemetry—not just prompt logs. Correlate activity across agents, tools, and MCP paths with multi-step execution context.
Runtime Inspection and Enforcement
Evaluate AI interactions at runtime by interpreting user intent. Inspect prompts, outputs, and model behavior against the purpose of the task. Analyze text, image, and PDF content with 18 built-in detectors to distinguish legitimate behavior from unusual or manipulated activity.
Policy Enforcement
Enforce policy decisions at runtime across agents, tools, and MCP connections. Apply guardrails to block, redact, restrict, or escalate actions based on what’s happening in the moment, without rewriting applications.
Incident Response and SOC Integration
Route AI security events into existing enterprise response workflows. Integrate with SIEM and SOAR platforms to support security operations center (SOC) workflows, governance escalation, and incident handling. Distinguish true security incidents from governance violations.
Forensics and Defensible Audit
Generate a forensic record for every AI interaction: who initiated it, what occurred, what data was used, which policies applied, and any enforcement actions. Full transaction reconstruction traces the entire user‑to‑outcome path, tied to the originating user and enriched with security context.
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FAQ
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How do enterprises secure AI adoption at scale?
To secure AI adoption at scale, enterprises must apply continuous runtime governance to every AI interaction, not simply control access. The core steps are:To secure AI adoption at scale, enterprises must apply continuous runtime governance to every AI interaction, not simply control access. The core steps are:
- Discover all AI usage. Surface every sanctioned, unsanctioned, embedded, and agent‑based AI system.
- Monitor runtime behavior. Track what AI systems actually do in multi‑step workflows.
- Enforce policies in real time. Block, redact, or restrict actions based on intent and context.
- Integrate with SOC workflows. Route AI‑related events to security information and event management (SIEM) or security orchestration, automation, and response (SOAR) tools for consistent incident handling.
- Maintain full forensic records. Capture who initiated an interaction, what happened, and which policies applied.
This approach gives organizations the visibility, control, and auditability needed to scale AI safely.
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What capabilities are required for enterprise AI runtime security?
Enterprise AI runtime security requires capabilities that let teams see how AI behaves, evaluate its intent, and control risky actions instantly. Key capabilities include:Enterprise AI runtime security requires capabilities that let teams see how AI behaves, evaluate its intent, and control risky actions instantly. Key capabilities include:
- AI discovery and inventory: Identifies every AI app, embedded feature, agent, tool, integration, and MCP server so nothing operates unseen.
- Runtime observability: Shows step‑by‑step AI behavior (agent → tool → MCP → external service) to reveal unusual or unsafe activity.
- Runtime inspection and enforcement: Detects threats such as prompt injection, data leakage, or overprivileged tool use. Blocks or restricts actions immediately.
- Centralized policy orchestration: Applies guardrails across agents and tools without requiring code changes, enabling scalable, consistent governance.
- SOC-aligned incident response: Sends AI events to SIEM or SOAR tools so analysts can triage and respond using existing workflows.
- Complete forensic audit trails: Records every interaction with full context to support investigations, compliance, and reporting.
These capabilities give enterprises real‑time control over AI behavior as adoption grows.
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Why is traditional cloud security insufficient for AI governance?
Traditional cloud security can’t govern AI because it controls access, not the real‑time behavior of AI models, agents, and tools. AI introduces risks that happen during execution, such as:Traditional cloud security can’t govern AI because it controls access, not the real‑time behavior of AI models, agents, and tools. AI introduces risks that happen during execution, such as:
- Prompt injection or manipulation
- Unintended tool calls or external API requests
- Data leakage through model outputs
- Multi‑step autonomous agent behavior
None of these appear in identity logs, configuration scans, or network rules. Thus, effective AI governance requires runtime inspection, intent understanding, and behavior‑aware policies. Traditional cloud security solutions were not designed to provide these.
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How can security teams detect and control shadow AI activity?
Security teams can control shadow AI by continuously discovering unmanaged AI usage and applying runtime guardrails to restrict risky behavior. The process includes:Security teams can control shadow AI by continuously discovering unmanaged AI usage and applying runtime guardrails to restrict risky behavior. The process includes:
- Discover all AI activity. Surface unsanctioned apps, embedded features, agents, and external AI services.
- Assess risk. Evaluate each system’s data exposure, permissions, and behavioral patterns.
- Apply runtime controls. Block unsafe actions, redact sensitive data, or restrict tool usage instantly.
- Integrate with SIEM or SOAR. Send events to existing SOC workflows to differentiate incidents from governance issues.
- Standardize approved usage. Transition shadow AI into governed, policy‑controlled workflows.
This reduces hidden risk while enabling responsible AI adoption.
