Business-application configuration securityAdditional if: AI features run inside business apps · AI features or approved AI activity inside business applications must be inventoried and reviewed.Action-taking agent safeguardsAdditional if: AI takes multi-step actions · The employee-facing AI performs multiple steps or takes actions rather than only generating content.Employee AI access and usage controlsDirectly addresses · Discover employee AI use and control access to approved and unapproved AI services.Browser and extension controlsAdditional if: Employees use a managed browser or extension · Employees use AI through a managed browser or browser extension where data and session controls can operate.AI data protectionDirectly addresses · Inspect prompts, responses, and files to prevent sensitive data from moving through AI tools.Agent identity and permissionsAdditional if: AI acts with delegated user access · An employee-facing AI can take actions through tools, connections, business applications, or a user's signed-in session.Machine and workload identityAdditional if: AI uses machine credentials · AI uses service accounts, API keys, machine identities, scheduled tasks, or connections owned by an agent.
Vendor research
Obsidian AI 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.
Use-case context
How this vendor relates to the selected use case
Related research availableBack 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.
- $90M known funding
- 51-200 employees
- Founded 2017
- Private-company revenue and profitability not sourced
Company context
Private independent company; reviewed Obsidian Security-controlled sources do not identify an acquirer or parent company
Obsidian says its platform processes more than 29 billion events monthly and serves large Fortune 500 and Global 2000 environments
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.
Founded2017
HeadquartersHeadquarters not stated on the reviewed Obsidian Security-controlled pages
OwnershipPrivate independent company; reviewed Obsidian Security-controlled sources do not identify an acquirer or parent company
Employees51-200
Capital and scaleIndependent company
Obsidian Security
- Known funding
- $90M
- Operating scale
- Obsidian says its platform processes more than 29 billion events monthly and serves large Fortune 500 and Global 2000 environments
- Backing context
- Obsidian Security says it raised a $90 million Series B in 2023 and names IVP, Norwest, and GV as backers
Series B
Named investors
IVP · Norwest · GV
Founders and leadership3 people listed
- Matt WolffCurrent role listed
Co-Founder & Chief AI Officer
- Glenn ChisholmCurrent role listed
Co-Founder & Director
- Ben JohnsonCurrent role listed
Co-Founder
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
- 2017
- Workforce scale
- 51-200
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Company sources and research limits6 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 sourceObsidian AI Security pageObsidian describes browser, prompt, agent, MCP, permission, runtime, and audit capabilities across enterprise AI use.Company sourceObsidian company pageObsidian provides its company timeline, funding milestone, investors, customer scale, and mission.Company sourceObsidian founding announcementObsidian's news archive identifies its 2017 founding team as Matt Wolff, Glenn Chisholm, and Ben Johnson.Company information sourceObsidian founder and board profileSupports the company facts shown in this profile.Company information sourceBen Johnson current company profileSupports the company facts shown in this profile.Company information sourceObsidian 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.
Business-application configuration securityCore product focusAction-taking agent safeguardsCore product focusEmployee AI access and usage controlsCore product focusBrowser and extension controlsCore product focusAI data protectionRelated coverageAI asset and configuration securityRelated coverageAI governance, risk, and complianceRelated coverageAgent identity and permissionsRelated coverageMachine and workload identityRelated coverageAI application runtime protectionRelated coverage
Buyer context
- Treat Obsidian as a software as a service (SaaS)-native AI and agent security platform spanning browser use, software as a service (SaaS) permissions, OAuth, non-human identity, runtime actions, and AI-SPM.
- Public evidence supports browser-level shadow AI, sensitive-prompt blocking, comprehensive agent inventory, Model Context Protocol (MCP) and model mapping, privilege right-sizing, execution-time guardrails, service-account resolution, and continuous audit evidence.
- Public pages reviewed did not establish formal AI red teaming, direct agent-to-agent authorization, AI FinOps, or a public licensing unit.
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
- 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
- 3
- 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
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
Strong public supportApply 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
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
Limited 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
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
No supporting claim foundAuthorize, 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
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.
Obsidian claims browser-level discovery of unapproved AI tools, extensions, personal accounts, and unmanaged-device use.
browser-level discovery
Obsidian claims continuous inventory of AI tools and agents across software as a service (SaaS), including software as a service (SaaS) permissions, OAuth connections, and shadow agents.
full visibility into every AI agent, its privileges, SaaS connections, and actions
Obsidian claims continuous inventory of every AI tool, agent, large language model (LLM), and Model Context Protocol (MCP) server with ownership and access context.
every AI tool, agent, LLM, and MCP server
Obsidian claims blocking high-risk unapproved AI use and agent actions through browser and runtime controls.
Block high-risk actions automatically at runtime
Obsidian claims blocking sensitive prompts before proprietary data leaves the browser for third-party generative AI platforms.
catching and blocking sensitive prompts at the source
Obsidian claims browser-level AI discovery and source-level blocking of sensitive prompts, including personal accounts and unmanaged devices.
before they ever leave the browser
Show 13 additional evidence records
Obsidian claims execution-time guardrails blocking privilege escalation, excessive data access, and policy violations.
Detect and block high-risk agent actions at execution time
Obsidian claims continuous audit-ready evidence of agent ownership, access, infrastructure, and runtime guardrail operation.
continuous, audit-ready evidence
Obsidian claims continuous OWASP-aligned risk scoring of agents when published or modified.
Continuous assessment maps every agent to OWASP risk factors
Obsidian claims detection of risky integrations, unapproved Model Context Protocol (MCP) connections, silent model swaps, and embedded credentials.
silent model swaps
Obsidian claims fine-grained execution-time guardrails using software as a service (SaaS) and Model Context Protocol (MCP) context to block unapproved actions.
Enforce guardrails directly at execution time
Obsidian claims real-time visibility into agent actions, tool calls, Model Context Protocol (MCP) servers, models, permissions, owners, and connected apps.
the actions they take, the tools they call on, and the permissions they are granted in real time
Obsidian materials reviewed did not provide a public claim for authenticating or authorizing direct agent-to-agent communication.
No quoted source text is recorded for this claim.
Obsidian claims resolution of agent identity to service accounts, application programming interface (API) tokens, OAuth privileges, embedded credentials, and connected software as a service (SaaS) applications.
the real service accounts it runs as
Obsidian claims tying agent actions to owners, executors, service accounts, permissions, OAuth access, and least-privilege enforcement.
Tie actions to the real owner and the executor for each agent
Obsidian claims runtime monitoring across Cursor, code environments, Model Context Protocol (MCP) servers, tools, and software as a service (SaaS) applications.
across Copilot, Claude, Cursor, and the SaaS apps agents touch
Obsidian materials reviewed did not provide a public claim for AI spend attribution, budgets, chargeback, rate limits, or token-cost anomaly detection.
No quoted source text is recorded for this claim.
Obsidian materials reviewed did not provide a public per-user, per-agent, event-volume, platform, or usage-based licensing unit for AI Security.
No quoted source text is recorded for this claim.
Obsidian claims out-of-the-box integrations across major AI platforms, software as a service (SaaS), cloud, endpoints, code, identity, and data systems.
across SaaS, cloud, endpoints, and code