No solution approach in the current research connects this vendor to this use case.
Vendor research
HiddenLayer
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.
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.
- $50M known funding
- 50-250 employees
- Founded 2022
Company context
Private independent company; no acquisition or parent-company claim found on reviewed HiddenLayer-controlled pages
HiddenLayer positions its AI Security Platform across AI Discovery, AI Supply Chain Security, AI Attack Simulation, and AI Runtime Security
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 11
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 8
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.
FoundedFounded by security researchers; founding year not stated on reviewed HiddenLayer-controlled pages
HeadquartersAustin, Texas
OwnershipPrivate independent company; no acquisition or parent-company claim found on reviewed HiddenLayer-controlled pages
Employees50-250
Capital and scaleIndependent company
HiddenLayer
- Known funding
- $50M
- Operating scale
- HiddenLayer positions its AI Security Platform across AI Discovery, AI Supply Chain Security, AI Attack Simulation, and AI Runtime Security
- Backing context
- Vendor-controlled pages reviewed cite patented technology and adversarial AI research; no investor/funding ownership claim was found on reviewed pages
Vendor-controlled pages reviewed cite patented technology and adversarial AI research; no investor/funding ownership claim was found on reviewed pages
Founders and leadership3 people listed
- Christopher "Tito" SestitoCurrent role listed
Co-Founder, CEO & Chairman
- Tanner BurnsCurrent role listed
Co-Founder & Chief Scientist
- Jim BallardCurrent role listed
Co-Founder & CIO
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
- Founded by security researchers; founding year not stated on reviewed HiddenLayer-controlled pages
- Workforce scale
- 50-250
- 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 limits3 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 sourceHiddenLayer homepageHiddenLayer says its platform provides AI Discovery, AI Supply Chain Security, AI Attack Simulation, and AI Runtime Security.Company sourceHiddenLayer about pageHiddenLayer says it was founded by security researchers and is dedicated to protecting intelligent systems.Company information sourceStored company websiteSupports 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
- Treat HiddenLayer as an AI model, application, agent, Model Context Protocol (MCP), and runtime security specialist rather than an employee browser/software as a service (SaaS) shadow-AI control.
- Public evidence supports AI asset inventory, model/application protection, guardrails, runtime monitoring, agentic workflow visibility, and Model Context Protocol (MCP)/tool-use protection.
- Public pages reviewed did not expose a pricing model; diligence should confirm packaging, deployment path, and required modules.
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 referenceCIS Critical Security Controls
- Requirements with public support
- 10
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 10
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 10
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 2
- 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.
- 01Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 02Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 03Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 04Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 05Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 06Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
- 07Generative AI application security
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
- 08Generative AI application security
A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
No supporting claim foundDiscover 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
No supporting claim foundInventory 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
Limited 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
Limited 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
Strong public supportDetect, 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
Strong 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
Limited 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
Strong public supportTest 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
Strong 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
Limited 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
No supporting claim foundInventory, 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
No supporting claim foundRegister 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
No supporting claim foundDiscover 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
No supporting claim foundVisibility, 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.
HiddenLayer materials reviewed did not provide a public claim for discovering employee use of third-party AI apps or employee prompt activity.
No quoted source text is recorded for this claim.
HiddenLayer materials reviewed did not provide a public claim for software as a service (SaaS) AI inventory or embedded third-party software as a service (SaaS) AI discovery.
No quoted source text is recorded for this claim.
HiddenLayer claims AI Discovery can reveal shadow AI, map ownership, and build a living inventory of AI across an enterprise.
The fastest, most complete way to reveal shadow AI, map ownership, and build a living inventory of all AI across your enterprise.
HiddenLayer claims AI Guardrails enforce policies that prevent prompt injection, data leakage, and unsafe AI behavior in real time.
AI Guardrails Enforce policies that prevent prompt injection, data leakage, and unsafe AI behavior in real time.
HiddenLayer claims AI Guardrails can prevent data leakage as part of real-time AI behavior policy enforcement.
AI Guardrails Enforce policies that prevent prompt injection, data leakage, and unsafe AI behavior in real time.
HiddenLayer materials reviewed did not provide a public claim for browser session control, software as a service (SaaS) user controls, or identity-aware software as a service (SaaS) activity protection.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
HiddenLayer claims AI Runtime Security monitors, detects, and responds to adversarial threats on agentic and generative AI applications.
Firewall to monitor, detect, and respond real-time to adversarial threats on agentic and generative AI applications.
HiddenLayer claims Agentic Runtime Visibility observes and reconstructs agent interactions across tools, data, and workflows in real time.
Agentic Runtime Visibility Observe and reconstruct agent interactions across tools, data, and workflows in real time.
HiddenLayer claims it protects autonomous and tool-using AI systems from misuse, escalation, and cross-system exploitation.
Protect autonomous and tool-using AI systems from misuse, escalation, and cross-system exploitation.
HiddenLayer materials reviewed did not provide a public claim for non-human identity, service-account, credential lifecycle, or AI-agent identity governance.
No quoted source text is recorded for this claim.
HiddenLayer materials reviewed did not provide a public claim for AI spend attribution, model cost routing, budget enforcement, rate limits, or token spend controls.
No quoted source text is recorded for this claim.
HiddenLayer materials reviewed did not provide a public per-user, per-seat, usage-based, or platform pricing model.
No quoted source text is recorded for this claim.
HiddenLayer claims native connectors for cloud, CI/CD, data platforms, SIEM/SOAR, application programming interface (API) gateways, and MLOps tools.
Native connectors for cloud, CI/CD, data platforms, SIEM/SOAR, API gateways, and MLOps tools.
HiddenLayer claims AI governance controls, risk management, AI bill-of-materials, model genealogy, and continuous posture reporting for model supply chains.
Enforce governance controls on model behavior.
HiddenLayer claims automated, continuous red teaming across large language models (LLMs), agents, and predictive models with scheduled or on-demand testing and vulnerability tracking.
Automatically run testing across LLMs, agents, and predictive models.
HiddenLayer claims model-file inspection, genealogy, AI bill-of-materials, and scanning for tampering, malware, vulnerabilities, backdoors, and integrity issues before production.
Scan proprietary, drop vendor, open-source, and third-party models for hidden vulnerabilities before they reach production.
HiddenLayer claims real-time AI guardrail policy enforcement and runtime monitoring and response for agentic and generative AI applications.
AI Guardrails Enforce policies that prevent prompt injection, data leakage, and unsafe AI behavior in real time.
HiddenLayer materials reviewed did not provide a public claim for agent registration, accountable ownership, delegated authorization, short-lived credentials, access review, or revocation.
No quoted source text is recorded for this claim.
HiddenLayer materials reviewed did not provide a public claim for governing coding-agent commands, workstation files or networks, integrated development environment (IDE) extensions, skills, hooks, secrets, or package actions.
No quoted source text is recorded for this claim.