Vendor research

Aurascape

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.

Use-case context

How this vendor relates to the selected use case

No related approach foundBack to use case →

These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.

AI spend and usageNo related approach

No solution approach in the current research connects this vendor to this use case.

Company scale

Growth stage
?

A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.

Company scale is separate from product features, effectiveness, and suitability.

$50M known funding
<50 employees
Founded 2024

Company context

Private independent company; no acquisition or parent-company claim found on reviewed Aurascape-controlled pages

Aurascape says it secures employee AI use, AI agent development, and production AI agents, and states 20,000+ AI applications secured

Vendor website ↗Add to comparison →

Research coverageCounts describe available public research, not product quality.View details

Vendor statements: 19 records
Source-checked records: 16
Evaluation requirements: 19 in this research model
Unresolved requirements: 3

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.

FoundedCompany page says Aurascape was created by people who secured the prior cloud era; founding year not stated on reviewed Aurascape-controlled pages

HeadquartersSanta Clara, California

OwnershipPrivate independent company; no acquisition or parent-company claim found on reviewed Aurascape-controlled pages

Employees<50

Capital and scaleIndependent company

Aurascape

Known funding: $50M
Operating scale: Aurascape says it secures employee AI use, AI agent development, and production AI agents, and states 20,000+ AI applications secured
Backing context: Vendor-controlled pages reviewed did not provide funding or investor ownership details

Founders and leadership5 people listed

Moinul Khan
Co-Founder & CEO
Current role listed
Patrick Xu
Co-Founder & CTO
Current role listed
Viswesh
Co-Founder, Product Management
Current role listed
Liang Li
Co-Founder, Engineering
Current role listed
Rajiv Khemani
Co-Founder & Board Member
Current role listed

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: Company page says Aurascape was created by people who secured the prior cloud era; founding year not stated on reviewed Aurascape-controlled pages
Workforce scale: <50
Hiring activity: Not displayed

Open research questions

A current hiring source is not available, so the count is not shown.

Company sources and research limits2 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 sourceAurascape homepageAurascape says it secures employee AI use, helps teams build agents safely, and protects production AI agents.Company sourceAurascape company pageAurascape's company page describes its mission for AI-era security and provides company contact context.

Solution areas

These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.

Employee AI access and usage controlsCore product focus Browser and extension controlsCore product focus AI data protectionCore product focus AI application runtime protectionRelated coverage Action-taking agent safeguardsRelated coverage AI asset and configuration securityRelated coverage

Buyer context

Treat Aurascape as an employee AI-use, embedded-software as a service (SaaS)-AI, data-protection, and agent-guardrail candidate.
Public evidence supports discovery of AI apps and agents, embedded software as a service (SaaS) AI, personal/risky usage control, intent decoding, sensitive-data protection, and tool-call/model-interaction governance.
Public pages reviewed did not expose pricing, AI FinOps, or non-human identity (NHI)/service-account lifecycle claims.

Related frameworks

Where public vendor statements relate to framework requirements

11 related frameworks · expand when needed

These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →

Current referenceCIS Critical Security Controls

Requirements with public support: 15
Related requirements: 11
References: 58

Requirements with public support: 15
Related requirements: 14
References: 71

Requirements with public support: 15
Related requirements: 12
References: 35

Requirements with public support: 15
Related requirements: 12
References: 71

Requirements with public support: 15
Related requirements: 13
References: 39

Requirements with public support: 15
Related requirements: 12
References: 39

Requirements with public support: 15
Related requirements: 12
References: 31

Requirements with public support: 15
Related requirements: 11
References: 27

Requirements with public support: 15
Related requirements: 13
References: 31

Requirements with public support: 15
Related requirements: 13
References: 25

Requirements with public support: 2
Related requirements: 3
References: 3

Review related requirements →

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.

01
Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
02
Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
03
AI-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.
04
AI-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.
05
Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
06
Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
07
Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
08
Controls 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

Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.

Controls for unapproved AI use

Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Detect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

Sensitive prompt, response, or file test data is detected and classified during an AI interaction.

Browser and business-application controls

Apply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

A test agent run captures plan, steps, tool calls, outcome, and timestamps.

Agent-to-agent communication security

Authorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Visibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.

No supporting claim found

Commercial Metadata NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 ISO/IEC 42001 OWASP 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

Publicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.

No supporting claim found

Commercial Metadata CSA AI Controls Matrix

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.

Open all vendor evidence →

AI governance, risk, and complianceSource checkedStrong public support for this requirement

Aurascape claims unified discovery, inventory, policy, compliance, human and agent governance, audit conversations, identity and intent context, approved registries, and cross-channel data lineage for employee AI and built agents.

One platform that covers both sides of enterprise AI agent risk.

AI assurance and adversarial testingSource checkedStrong public support for this requirement

Aurascape claims predeployment adversarial guardrail tests for prompt injection and jailbreak attempts plus code-path vulnerability checks against known CVEs.

Runs adversarial guardrail tests and code-path vulnerability checks before an agent ships.

AI model and supply-chain securitySource checkedStrong public support for this requirement

Aurascape claims discovery of agents and Model Context Protocol (MCP) servers, a vetted custom registry, tool-poisoning detection, approved-call signing, unsigned-call blocking, and project-configuration supply-chain protection.

Whitelist approved MCP servers and tools in a custom registry so only vetted endpoints are reachable.

AI gateway, tool-connection, and runtime controlsSource checkedStrong public support for this requirement

Aurascape claims an AI Proxy and Zero Bypass Model Context Protocol (MCP) Gateway that inspect prompts, responses, intent, tool calls, parameters, data exchanges, and results; sign approved calls; and block or sanitize policy violations before execution.

The Zero Bypass MCP Gateway signs approved tool calls and blocks unsigned ones.

AI agent identity and permissionsSource checkedLimited public support for this requirement

Aurascape claims context-aware tool policy based on user identity, account type, agent intent, entitlement, data sensitivity, OAuth roles and scopes, and approved-call signatures.

Context-aware policy on identity, intent, entitlement, and data sensitivity.

AI coding-agent and workstation securitySource checkedStrong public support for this requirement

Aurascape claims local discovery for Claude Code and Cursor, source-code and unsafe-code protections, predeployment code-path testing, Model Context Protocol (MCP) registry and signing, tool-call enforcement, and data-lineage controls from first code through runtime.

Secure every agent from first line of code through production runtime.

Show 13 additional evidence records

Unapproved AI use discoverySource checkedStrong public support for this requirement

Aurascape claims it can automatically uncover every AI app and agent in use and stop personal or risky usage.

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.

AI-feature discovery in business applicationsSource checkedStrong public support for this requirement

Aurascape claims it uncovers AI apps and agents embedded inside software as a service (SaaS) applications.

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.

Approved AI usage monitoringSource checkedStrong public support for this requirement

Aurascape claims it decodes prompts, responses, user identity, and intent for AI interactions.

Decode prompts, responses, user identity, and intent

Controls for unapproved AI useSource checkedStrong public support for this requirement

Aurascape claims it applies policy based on role, sensitivity, and conversation context.

Apply policy based on role, sensitivity, and conversation context

Sensitive-data protection for generative AISource checkedStrong public support for this requirement

Aurascape claims it detects sensitive information in AI tools in real time and applies contextual controls to prevent leakage or misuse.

Detect sensitive information flowing through AI tools in real time and apply contextual, intent-based controls to prevent leakage or misuse.

Browser and business-application controlsSource checkedStrong public support for this requirement

Aurascape claims it secures employee AI use across apps, browsers, copilots, and agents.

Aurascape helps organizations safely adopt AI without slowing the business down. We secure employee AI use across apps, browsers, copilots, and agents

Generative AI application securitySource checkedStrong public support for this requirement

Aurascape claims it helps organizations securely build and operate AI agents and applications with controls from development to runtime.

We help teams reduce risk across the AI lifecycle with controls that protect data, govern behavior, and strengthen security from development to runtime.

Action-taking agent monitoringSource checkedStrong public support for this requirement

Aurascape claims full visibility, adversarial testing, and continuous governance across every tool call and model interaction for agents.

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.

Agent-to-agent communication securitySource checkedLimited public support for this requirement

Aurascape claims continuous governance across every tool call and model interaction for agents.

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.

Non-human identity and service-account securityNo supporting claim found

Aurascape materials reviewed did not provide a public claim for non-human identity (NHI), service-account, credential lifecycle, or AI-agent identity governance.

No quoted source text is recorded for this claim.

AI cost and usage controlsNo supporting claim found

Aurascape materials reviewed did not provide a public claim for AI spend attribution, model routing cost control, budgets, rate limits, or runaway token controls.

No quoted source text is recorded for this claim.

Licensing modelNo supporting claim found

Aurascape materials reviewed did not provide a public per-user, per-seat, platform, usage-based, or enterprise pricing model.

No quoted source text is recorded for this claim.

Approved AI platform contextSource checkedStrong public support for this requirement

Aurascape says it provides visibility, policy control, and guardrails so teams can safely use and build AI apps and agents.

Aurascape provides the visibility, policy control, and guardrails you need so teams can safely use and build AI apps and agents.

Aurascape

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.

Private independent company; no acquisition or parent-company claim found on reviewed Aurascape-controlled pages

Aurascape

Where public vendor statements relate to framework requirements

Vendor statements and quoted evidence

Aurascape claims unified discovery, inventory, policy, compliance, human and agent governance, audit conversations, identity and intent context, approved registries, and cross-channel data lineage for employee AI and built agents.

Aurascape claims predeployment adversarial guardrail tests for prompt injection and jailbreak attempts plus code-path vulnerability checks against known CVEs.

Aurascape claims discovery of agents and Model Context Protocol (MCP) servers, a vetted custom registry, tool-poisoning detection, approved-call signing, unsigned-call blocking, and project-configuration supply-chain protection.

Aurascape claims an AI Proxy and Zero Bypass Model Context Protocol (MCP) Gateway that inspect prompts, responses, intent, tool calls, parameters, data exchanges, and results; sign approved calls; and block or sanitize policy violations before execution.

Aurascape claims context-aware tool policy based on user identity, account type, agent intent, entitlement, data sensitivity, OAuth roles and scopes, and approved-call signatures.

Aurascape claims local discovery for Claude Code and Cursor, source-code and unsafe-code protections, predeployment code-path testing, Model Context Protocol (MCP) registry and signing, tool-call enforcement, and data-lineage controls from first code through runtime.

Aurascape claims it can automatically uncover every AI app and agent in use and stop personal or risky usage.

Aurascape claims it uncovers AI apps and agents embedded inside software as a service (SaaS) applications.

Aurascape claims it decodes prompts, responses, user identity, and intent for AI interactions.

Aurascape claims it applies policy based on role, sensitivity, and conversation context.

Aurascape claims it detects sensitive information in AI tools in real time and applies contextual controls to prevent leakage or misuse.

Aurascape claims it secures employee AI use across apps, browsers, copilots, and agents.

Aurascape claims it helps organizations securely build and operate AI agents and applications with controls from development to runtime.

Aurascape claims full visibility, adversarial testing, and continuous governance across every tool call and model interaction for agents.

Aurascape claims continuous governance across every tool call and model interaction for agents.

Aurascape materials reviewed did not provide a public claim for non-human identity (NHI), service-account, credential lifecycle, or AI-agent identity governance.

Aurascape materials reviewed did not provide a public claim for AI spend attribution, model routing cost control, budgets, rate limits, or runaway token controls.

Aurascape materials reviewed did not provide a public per-user, per-seat, platform, usage-based, or enterprise pricing model.

Aurascape says it provides visibility, policy control, and guardrails so teams can safely use and build AI apps and agents.

Growth stage
?