Holistic AI
What is Holistic AI?
Holistic AI is an enterprise AI governance platform for teams that need continuous visibility and control across AI systems. It combines AI Discovery, Inventory, Monitoring, Risk Management, LLM Testing, Bias Audit, Policies & Controls, and Compliance to scan clouds, code repositories, documents, and agent workflows, then enforce guardrails and keep evidence current. It is used by Unilever, Mapfre, and Allegis.
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At a glance
- Holistic AI is best for enterprise governance teams who need continuous visibility and control over AI systems.
What does Holistic AI do?
Holistic AI connects discovery, testing, and enforcement into one governance workflow for enterprise AI. It scans clouds, code repositories, documents, and agent workflows to build a living inventory, then runs risk analysis, bias auditing, red teaming, and continuous monitoring to surface issues before they reach production. The platform also turns policies into real-time guardrails, so teams can intervene on risky outputs and keep compliance evidence current. The system is built for scale: its Identify view shows 3 live AI inventory items, 3 ML models, 0 AI agents, and 0 pipelines in one example, while Protect cites 100+ automated tests and 40+ AI/ML test types. Holistic AI says it supports 20+ integrations and 24/7 scanning with real-time indexing, and its customer stories include Unilever, Starling Bank, Wikimedia Foundation, and Allegis Group. Enterprise security is read-only and metadata-only, with client-dedicated tenancy and no production workload impact.
Why use Holistic AI?
- It combines discovery, risk testing, and enforcement in one workflow, reducing handoffs between separate governance tools.
- Read-only, metadata-only collection lets teams inspect AI systems without touching prompts, training data, or production workloads.
- Continuous scanning and real-time indexing keep the inventory current as systems change, drift, or expand.
- Automated tests and guided remediation help teams move from finding risk to fixing it inside the same platform.
- Policy guardrails and audit reporting make governance operational instead of document-based.
Who is Holistic AI for?
- AI governance leaders who need a single inventory of models, agents, and shadow AI.
- Risk and compliance teams who need audit-ready evidence and regulatory alignment.
- Security teams who need continuous testing and monitoring for AI attack surfaces.
- ML platform owners who need read-only discovery across clouds, code, and documentation.
- Legal and policy teams who need enforceable controls and traceable reporting.
What are Holistic AI's key features?
AI Discovery
Scans cloud environments, code repositories, and active connections to detect embedded AI and shadow AI across 230+ cloud platforms.
Inventory
Builds a centralized AI inventory with asset reconciliation and ontology relationships, so teams can track models, pipelines, and agents in one place.
Monitoring
Provides 24/7 scanning with real-time indexing and sub-second detection, helping teams catch new AI assets and changes before they create risk.
Risk Management
Scores AI across 40+ risk dimensions with heat maps, trend analysis, and continuous risk monitoring to prioritize remediation work.
LLM Testing
Runs 40+ AI/ML test types, including hallucination detection, prompt injection, and data exfiltration checks, to validate model behavior before release.
Bias Audit
Measures bias with automated tests and audit outputs, supporting compliance reviews and helping teams document fairness issues across AI systems.
Policies & Controls
Uses a visual policy builder, custom rule engine, and kill switches to enforce guardrails, block unsafe actions, and manage versioned controls.
Compliance
Maps AI activity to regulatory templates and compliance assessments, with automated audit trails and reporting for frameworks such as the EU AI Act.
What does Holistic AI integrate with?
- AWS
- Azure
- GitHub
- Databricks
- Google Cloud
- LangSmith
- GitLab
- Bitbucket
- SharePoint
- Confluence
- Snowflake
- ServiceNow
- Langfuse
- CrewAI
- Copilot Studio
- Zscaler
- on-prem
- OpenAI
- Anthropic
- SageMaker
- Azure ML
- Vertex AI
- Lambda
- BQ
- Slack
- Notion
What are Holistic AI's use cases?
Governance inventory for AI leaders
AI governance leaders use Holistic AI to build a single view of models, agents, and shadow AI across the business. They rely on AI Discovery and Centralised Inventory to surface what exists, then use AI Discovery & Inventory to keep the register current as new tools appear.
Audit readiness for compliance teams
Risk and compliance teams use Holistic AI to prepare evidence for reviews and align controls to policy. They use Compliance, Audit & Reporting, and Compliance Mapping to produce traceable records, then lean on Regulatory Alignment to show where gaps still need action.
Continuous testing for security teams
Security teams use Holistic AI to probe AI attack surfaces before issues reach production. They run LLM Testing and AI Red Teaming to catch prompt injection, data exfiltration, and jailbreak risks, then use Continuous Monitoring to keep watch after launch.
Read-only discovery for platform owners
ML platform owners use Holistic AI to discover AI assets across clouds, code, and documentation without disrupting teams. They connect Source Connections and Source Indexing to scan repositories and environments, then use Observability & Audit to reconcile findings into one governed view.
How does Holistic AI work?
- Connect your first Source Connections to AWS, Azure, GitHub, or Google Cloud, then let Source Indexing scan repositories, cloud environments, and documentation for AI artifacts.
- Review the AI Discovery & Inventory view to reconcile models, agents, and shadow AI into a Centralised Inventory, using Asset Reconciliation to remove duplicates and gaps.
- Run LLM Testing and AI Red Teaming on exposed systems, then inspect Risk Assessment results and Multi-Dimensional Risk Scoring to prioritize the highest-impact issues.
- Set Policies & Controls with the Visual Policy Builder, add Regulatory Templates or custom rules, and use Real-Time Guardrails to block unsafe behavior.
- Track Continuous Monitoring and Compliance Monitoring in Reporting dashboards, then export Automated Audit Trails and Regulatory Reporting for ongoing reviews.
Frequently asked questions
What is Holistic AI?
Holistic AI is an enterprise AI governance platform for teams that need continuous visibility and control across AI systems. It combines AI Discovery, Inventory, Monitoring, Risk Management, LLM Testing, Bias Audit, Policies & Controls, and Compliance to scan clouds, code repositories, documents, and agent workflows, then enforce guardrails and keep evidence current. It is used by Unilever, Mapfre, and Allegis.
What is Holistic AI used for? Who is it for?
Holistic AI is used for AI Discovery, Inventory, and Monitoring. It's built for AI governance leaders, Risk and compliance teams, and Security teams.
Does Holistic AI have an API and what does it integrate with?
Holistic AI doesn't publish a public API. It integrates with AWS, Azure, GitHub, Databricks, Google Cloud, and 21 more.
Editor's read
Check whether your rollout needs the platform's read-only, metadata-only collection model. If you need a tool that touches prompts, training data, or production workloads, that constraint matters before signup.
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