Vendor research
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
A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company context
Aurascape says it secures employee AI use, AI agent development, and production AI agents, and states 20,000+ AI applications secured
Company intelligence
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Vendor-controlled pages reviewed did not provide funding or investor ownership details
Co-Founder & CEO
Co-Founder & CTO
Co-Founder, Product Management
Co-Founder, Engineering
Co-Founder & Board Member
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
A current count requires a retained, clickable source URL.
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
Related frameworks
Evaluation questions
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.
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
The test user's AI usage activity can be filtered or exported with AI-specific context.
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.
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
The control event records policy reason, user, destination, action, and timestamp.
Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
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.
Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Detect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Apply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Inventory 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.
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
Test 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.
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
Discover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
Mediate 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.
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Authorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
Register 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.
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
Discover 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.
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
Visibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Publicly 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
Showing the first 6 of 19 source records. Open additional records only when needed.
One platform that covers both sides of enterprise AI agent risk.
Runs adversarial guardrail tests and code-path vulnerability checks before an agent ships.
Whitelist approved MCP servers and tools in a custom registry so only vetted endpoints are reachable.
The Zero Bypass MCP Gateway signs approved tool calls and blocks unsigned ones.
Context-aware policy on identity, intent, entitlement, and data sensitivity.
Secure every agent from first line of code through production runtime.
Automatically uncover every AI app and agent in use, even those embedded inside SaaS apps, and stop personal or risky usage before it creates exposure.
Automatically uncover every AI app and agent in use, even those embedded inside SaaS apps, and stop personal or risky usage before it creates exposure.
Decode prompts, responses, user identity, and intent
Apply policy based on role, sensitivity, and conversation context
Detect sensitive information flowing through AI tools in real time and apply contextual, intent-based controls to prevent leakage or misuse.
Aurascape helps organizations safely adopt AI without slowing the business down. We secure employee AI use across apps, browsers, copilots, and agents
We help teams reduce risk across the AI lifecycle with controls that protect data, govern behavior, and strengthen security from development to runtime.
Secure every agent from the first line of code to production runtime, with full visibility, adversarial testing, and continuous governance across every tool call and model interaction.
Secure every agent from the first line of code to production runtime, with full visibility, adversarial testing, and continuous governance across every tool call and model interaction.
No quoted source text is recorded for this claim.
No quoted source text is recorded for this claim.
No quoted source text is recorded for this claim.
Aurascape provides the visibility, policy control, and guardrails you need so teams can safely use and build AI apps and agents.