Public-source research
Vendor evidence
Review what vendors say publicly, the exact quoted source text, the security requirement each statement may support, and what still needs verification.
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Evidence records1280
Source checked863
Needs verification0
Source captured0
No supporting claim found411
Excluded from evidence6
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
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.
Related framework references (5)
No public claim found for this capability.
No quoted source text is recorded for this claim.
Related framework references (5)
No public claim found for this capability.
No quoted source text is recorded for this claim.
Related framework references (5)
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
Related framework references (5)
No public claim found for this capability.
No quoted source text is recorded for this claim.
Related framework references (2)
No public claim found for this capability.
No quoted source text is recorded for this claim.
Related framework references (5)