CYBER BUYER

AI Security & AI Governance

JetStream Security

A security-first AI governance platform that connects AI discovery, approved workflow design, identity controls, and runtime enforcement to keep enterprise AI accountable and within policy.

EmergingAI SecurityAI GovernanceAgentic IdentityRuntime GovernanceFinOpsMCP ServersAI Kill Switch
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Best for

Enterprises securing and governing AI agents in production through runtime policy enforcement, identity accountability, and continuous visibility.

Overview

JetStream Security is an enterprise AI governance and security platform that helps organizations control, secure, and scale agentic AI in production. It provides continuous visibility across AI models, agents, copilots, tools, and workflows, enabling teams to define and approve AI behavior while linking every action to an accountable human, AI agent, or other non-human identity (NHI). Through runtime governance, JetStream enforces policy guardrails, detects AI drift, manages risk, and supports audit-ready compliance and AI cost accountability. The platform helps enterprises operate AI as a governed, observable, and trusted system.

The problem it solves

Enterprise AI adoption can outpace security oversight. Models, agents, tools and credentials spread across teams without a clear inventory, approved operating boundaries or accountable owners. JetStream Security addresses this gap by connecting AI discovery, system design, identity and runtime governance so organizations can identify unapproved AI use, detect deviations from intended behavior and bring AI usage and costs under control.

Core capabilities

  • AI discovery and inventory: identify models, agents, MCP servers, applications, datasets, tools and associated identities across supported environments.
  • Approved AI system blueprints: map how AI components work together and document the intended design and operating boundaries.
  • Agentic identity and access accountability: associate human, agent and non-human identities with owners, with brokered keys and attribute-based access controls.
  • Runtime governance: compare live activity with approved designs, detect drift and unexpected behavior, and apply policy controls with activity logging.
  • AI FinOps visibility: track AI usage, token consumption and spend to help teams understand costs and assign accountability.

Ideal customer profile

Enterprises moving AI agents, copilots and AI-enabled workflows from experimentation into production. A strong fit is an organization with multiple AI tools or teams that needs visibility into AI use, accountable ownership, runtime controls and audit evidence. Security and risk leaders, identity teams, and AI builders or platform teams should evaluate the platform together against their actual environments and operating requirements.

Use cases

Discover sanctioned and shadow AI across the organizationDocument and approve agentic AI system designsLink agent actions and credentials to accountable ownersDetect runtime drift and apply AI policy guardrailsCollect AI activity evidence for audits and incident responseAttribute AI token usage and spend to teams or systems

Why CYBER BUYER is featuring this vendor

CYBER BUYER has signed a reseller agreement with JetStream Security. We are featuring the vendor for its security-first approach to governing enterprise AI: connecting discovery, approved system designs, agent identity, runtime controls and cost accountability. It gives security leaders and AI builders a focused option to evaluate as agentic workflows move into production. CYBER BUYER can help buyers assess fit, validate the required controls and coordinate procurement; the profile is not a guarantee of product performance.

Questions to ask during evaluation

  • Which AI models, agents, MCP servers, tools and environments can you discover in our stack, and what deployment or configuration does each method require?
  • Which policies can actively block or modify actions at runtime, where are they enforced, and what happens if an enforcement component is unavailable?
  • How are approved AI blueprints created and maintained, what counts as drift, and how do teams investigate false positives?
  • Which identity systems and credential types are supported, and how are ownership, key storage, rotation, permissions and revocation managed?
  • What telemetry is collected, where is it stored, who can access it, and what retention and export options support our audit requirements?
  • Which providers, token metrics and cost dimensions are covered, and can budgets be enforced or is usage only reported?
  • What latency, operational dependencies and implementation work should we expect, and how can we validate them in a pilot?
  • What deployment options and independent security assurance materials are available, and how are pricing, onboarding and ongoing support structured?

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