Company context
Private, VC-backed
Early-stage company led by former CrowdStrike, SentinelOne, Cohesity, Dazz, and enterprise security operators
Vendor research
JetStream Security
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Company scale
Growth stage?
Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.
?
Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- $34M known funding
- 51-200 employees
- Founded 2025
- Private-company revenue and profitability not sourced
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 14
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 4
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Founded2026 public launch
HeadquartersUnited States; headquarters address not prominent on public site
OwnershipPrivate, VC-backed
Employees51-200
Capital and scaleIndependent company
JetStream Security
- Known funding
- $34M
- Operating scale
- Early-stage company led by former CrowdStrike, SentinelOne, Cohesity, Dazz, and enterprise security operators
- Backing context
- $34M seed led by Redpoint Ventures with CrowdStrike Falcon Fund and notable security operators participating
Seed · $34M
Named investors
Redpoint Ventures · CrowdStrike Falcon Fund
Founders and leadership4 people listed
- Raj RajamaniCurrent role listed
Founder & CEO
- Jared PhippsCurrent role listed
Founder & COO
- Venu VissamsettyCurrent role listed
Founder & Chief Architect
- Jatheen (AJ) AnandCurrent role listed
Founder & CTO
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- 2026 public launch
- Workforce scale
- 51-200
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- A current hiring source is not available, so the count is not shown.
Company sources and research limits4 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourceJetStream funding announcementJetStream announced a $34M seed investment and named its founding leadership team.Company sourceJetStream about pageJetStream describes its mission, leadership, and investor/advisor network.Company information sourceStored company websiteSupports the company facts shown in this profile.Company information sourceJetStream Security LinkedIn company profileSupports the company facts shown in this profile.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Strong operator pedigree and fresh capital, but buyer diligence should test customer references and product maturity closely.
- Particularly relevant for AI governance, identity, accountability, and cost-control positioning around agentic systems.
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCSA AI Controls Matrix
- Requirements with public support
- 14
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 4
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- 02Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
- 03AI-feature discovery in business applications
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 05Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 06Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 07Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 08Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Strong public supportDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
Strong public supportInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
Strong public supportMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
Strong public supportBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
No supporting claim foundDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
No supporting claim foundApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
Limited public supportProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
Strong public supportInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
No supporting claim foundTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
Limited public supportDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
Strong public supportMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Strong public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
Limited public supportAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
Strong public supportInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
Strong public supportRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
Limited public supportDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
Strong public supportVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
No supporting claim foundPublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 19 source records. Open additional records only when needed.
JetStream SAIG claims AI estate discovery, approved design control, accountable ownership, runtime governance, drift detection, audit-ready evidence, policy boundaries, and cost accountability across humans, agents, NHIs, models, tools, Model Context Protocol (MCP), software as a service (SaaS), and cloud.
Every AI action is attributed to accountable owners and kept inside approved boundaries.
JetStream SAIG materials reviewed did not provide a public product claim for automated adversarial testing, repeatable attack suites, model or agent red teaming, or release-gate evaluation.
No quoted source text is recorded for this claim.
JetStream claims visibility into model swaps, toolchain expansion, Model Context Protocol (MCP) servers, open-source components, configurations, credentials, permissions, connections, and runtime drift with Verified Model Context Protocol (MCP) and approved-design governance.
Surface model changes, new MCP usage, and behavioral deviations the moment they occur.
JetStream claims inline runtime enforcement of approved AI workflow designs across agents, tools, models, Model Context Protocol (MCP) servers, data, permissions, and identities with continuous behavioral telemetry and drift response.
Enforces approved designs inline and continuously captures behavioral telemetry.
JetStream claims an accountable identity fabric binding humans, agents, service accounts, and models with short-lived identity-scoped keys, ownership attribution, permission monitoring, and runtime enforcement.
Bind every AI action to an accountable identity using short-lived, identity-scoped keys.
JetStream claims endpoint scanning for AI applications, Model Context Protocol (MCP) servers, cleartext keys, configuration artifacts, coding-assistant toolchains, permissions, and runtime actions with identity attribution and approved-design enforcement.
The scanner looks throughout user directories to identify AI apps, MCP servers, cleartext API and license keys, and other AI artifacts.
Show 13 additional evidence records
JetStream claims AI Visibility continuously discovers and inventories AI agents, models, tools, and workflows across software as a service (SaaS), endpoints, cloud, application programming interfaces (APIs), and internal systems.
Continuously discover and inventory AI agents, models, tools, and workflows across SaaS, endpoints, cloud, APIs, and internal systems.
JetStream claims it can uncover shadow AI and inventory AI agents, models, tools, and workflows across software as a service (SaaS), endpoints, cloud, application programming interfaces (APIs), and internal systems.
Uncover shadow AI. Continuously discover and inventory AI agents, models, tools, and workflows across SaaS, endpoints, cloud, APIs, and internal systems.
JetStream claims it lets organizations see every AI action, tie actions to accountable owners, and keep workflows within approved boundaries.
See every AI action, tie actions to accountable owners, keep workflows inside approved boundaries, and turn AI from a black box into a managed system.
JetStream claims Runtime Governance enforces operational and security guardrails and lets teams approve changes or stop runs when behavior varies from an approved Blueprint.
JetStream watches live AI activity and compares that to the approved design within each AI Blueprint. It enforces operational and security guardrails, records evidence, and flags drift from each workflow’s operational contract the moment behavior varies from the Blueprint. Teams can approve the change or stop the run—without losing auditability.
JetStream claims Design Control maps agents, models, tools, datasets, and identities and how they interact, then compares intended design to runtime reality as systems evolve.
Design Control turns raw discovery into an approved design by mapping how your AI actually works—agents, models, tools, datasets, and identities—and how they interact. It becomes a living operational contract with versioning and change control, so teams can document intent and compare it to reality as systems evolve.
JetStream claims least-privilege authorization travels with workflows across agent hand-offs and keeps behavior inside approved designs.
Least‑privilege, just‑in‑time authorization travels with the workflow across agent hand‑offs, keeping behavior inside the approved design.
JetStream claims every AI workflow is tied to a provable owner by binding people, agents, NHIs, and model usage into an accountable identity fabric.
Every AI workflow is tied to a provable owner by binding people, agents, NHIs, and model usage into one accountable identity fabric. Raw model provider secrets are replaced with virtual, revocable keys scoped to the approved Blueprint design, so you can rotate, rate‑limit, or kill access without code changes.
JetStream claims AI FinOps accountability for usage economics by model, agent, workflow, and owner, with anomalous-burn detection, budget and rate-limit controls, and model-routing optimization.
JetStream turns AI usage into clear, actionable economics — by model, agent, workflow, and owner.
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.
JetStream claims it systematizes how agentic systems are assembled by introducing intent, structure, and risk context early.
systematize how agentic systems are actually assembled. Go beyond simple AI registries to introduce intent, structure, and risk context early
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.