AI governance, risk, and complianceDirectly addresses · Manage ownership, policies, risk reviews, approvals, exceptions, obligations, and audit evidence.AI asset and configuration securityDirectly addresses · Discover approved, unmanaged, and action-taking AI systems along with model and software artifacts that require governance.Agent identity and permissionsAdditional if: Governed agents have delegated authority · The governed environment includes action-taking agents with delegated authority.
Vendor research
Holistic AI
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
Emerging?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.This is a company-scale signal, not a product-quality rating.
?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.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.
- 11-50 employees
- Founded 2020
- Private-company revenue and profitability not sourced
Company context
Private independent company; reviewed Holistic AI-controlled sources do not identify an acquirer or parent company
Holistic AI names Unilever, Michelin, and Adecco as customers and reports governing more than 300 AI projects for Unilever
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 15
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 4
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.
Founded2020
HeadquartersLondon, United Kingdom
OwnershipPrivate independent company; reviewed Holistic AI-controlled sources do not identify an acquirer or parent company
Employees11-50
Capital and scaleIndependent company
Holistic AI
- Known funding
- Amount not disclosed
- Operating scale
- Holistic AI names Unilever, Michelin, and Adecco as customers and reports governing more than 300 AI projects for Unilever
- Backing context
- Private venture-backed company; official and investor sources reviewed identify Tola Capital and Mozilla Ventures, but no reliable aggregate funding total was found
Undisclosed equity
Founders and leadership2 people listed
- Emre KazimCurrent role listed
Co-Founder & Co-CEO
- Adriano KoshiyamaCurrent role listed
Co-Founder & Co-CEO
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
- 2020
- Workforce scale
- 11-50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- Private-company funding total is not yet supported by a public source.
Company sources and research limits8 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 sourceHolistic AI platform pageHolistic AI describes Identify, Protect, and Enforce product pillars across AI governance, discovery, testing, and runtime controls.Company sourceHolistic AI Lab pageHolistic AI says its research is embedded into its enterprise AI governance platform.Company sourceHolistic AI executive bioHolistic AI identifies Emre Kazim and Adriano Koshiyama as current co-founders and co-CEOs and dates the company to 2020.Company sourceUK Companies HouseThe UK Companies House record lists Holistic AI Limited's registered office in London.Company sourceHolistic AI Unilever case studyHolistic AI reports governing more than 300 AI projects across Unilever and describes enterprise-wide compliance and risk-management outcomes.Company information sourceHolistic AI executive biosSupports the company facts shown in this profile.Company information sourceHolistic AI LinkedIn company profileSupports the company facts shown in this profile.Company information sourceHolistic AI financing 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.
AI governance, risk, and complianceCore product focusAI testing and adversarial assuranceCore product focusAI asset and configuration securityRelated coverageAI application runtime protectionRelated coverageAI gateway and tool-connection controlsRelated coverageAction-taking agent safeguardsRelated coverageAgent identity and permissionsRelated coverage
Buyer context
- Treat Holistic AI as a combined AI governance, discovery, assurance, and runtime-enforcement platform rather than a documentation-only GRC product.
- Named global-enterprise deployments strengthen the adoption signal, but reviewed sources do not establish total funding, revenue, profitability, or an operating scale suitable for default enterprise procurement.
- Public evidence supports cloud, code, data, and software as a service (SaaS) AI discovery; agent graphs; red teaming; continuous testing; regulatory workflows; runtime guardrails; kill switches; and agent-to-agent controls.
- Public pages reviewed did not expose a platform licensing unit or generic service-account and machine-credential lifecycle controls.
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 referenceCSA AI Controls Matrix
- Requirements with public support
- 14
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 27
- 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
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
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
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
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
Strong 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
Limited 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
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
Limited public supportVisibility, 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.
Holistic AI claims continuous discovery of shadow AI across cloud, code, data, and software as a service (SaaS) tools.
Automatically scan your cloud, code, data and SaaS tools to detect every AI model, API, agent and pipeline.
Holistic AI claims read-only connections to software as a service (SaaS) tools that scan for AI models, application programming interface (API) calls, agents, workflows, and pipelines.
Holistic AI connects read-only to your cloud environments (AWS, Azure, GCP), code repositories (GitHub, GitLab), data platforms (Snowflake, Databricks), and SaaS tools.
Holistic AI claims a continuously updated AI inventory with model, data-source, owner, and risk metadata.
Full metadata covering model type, data sources, owners and risk scores
Holistic AI claims automated blocking, kill switches, access revocation, and human-review escalation when policy violations or anomalies are detected.
blocking unsafe outputs, activating kill switches, revoking access or flagging for human review
Holistic AI claims privacy-leak detection and data-exposure analysis as part of continuous AI risk management.
Privacy leak detection and data exposure analysis
Holistic AI materials reviewed did not provide a public claim for browser session controls over upload, download, copy, paste, sharing, or form submission.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Holistic AI claims runtime guardrails across models, agents, application programming interfaces (APIs), workflows, and generative AI applications.
Deploy runtime guardrails across models, agents, APIs and workflows.
Holistic AI claims audit-ready reporting mapped to EU AI Act, ISO 42001, and NIST AI RMF requirements.
Export audit ready reports for any framework instantly
Holistic AI claims automated AI red teaming with more than 100 attack vectors and retesting after remediation.
AI red teaming with over 100 automated attack vectors
Holistic AI claims lineage and dependency mapping across data sources, models, application programming interfaces (APIs), pipelines, and AI outputs.
Full lineage tracking from data source to AI output
Holistic AI claims runtime enforcement of tool allowlists, access controls, and cost limits across agents and sessions.
enforce tool-calling allowlists, access controls, and cost limits across agents and sessions
Holistic AI claims an interactive graph that maps agent relationships, workflows, dependencies, and agent-to-agent chains.
Interactive agent graph that maps all AI agent relationships
Holistic AI claims guardrails over agent-to-agent communication, tool use, and decision chains.
enforcing guardrails on agent to agent communication, tool use and decision chains
Holistic AI materials reviewed did not provide a public claim for generic workload identities, service accounts, application programming interface (API) keys, secret rotation, or machine-credential lifecycle management.
No quoted source text is recorded for this claim.
Holistic AI claims tracking of agent identity, reasoning chains, and tool calls with runtime access controls.
Track every agent's identity, reasoning chain, and tool calls
Holistic AI materials reviewed did not provide a public claim for controlling coding-agent commands, filesystem or network actions, skills, hooks, integrated development environment (IDE) extensions, packages, or workstation activity.
No quoted source text is recorded for this claim.
Holistic AI claims cost-limit enforcement across agents and sessions.
cost limits across agents and sessions
Holistic AI materials reviewed did not provide a public per-user, per-system, per-agent, usage-based, or platform licensing unit for the AI Governance Platform.
No quoted source text is recorded for this claim.
Holistic AI claims application programming interface (API) and SDK integration across cloud, data, code, software as a service (SaaS), model, and agent environments without replacing the existing stack.
Holistic AI integrates via API and SDK with no infrastructure changes required.