Company context
Private independent company; reviewed Credo AI-controlled sources do not identify an acquirer or parent company
Credo AI says its customers include Global 2000 and Fortune 500 enterprises
Vendor research
Credo 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.
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 2020
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 14
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 5
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
HeadquartersUnited States
OwnershipPrivate independent company; reviewed Credo AI-controlled sources do not identify an acquirer or parent company
Employees50-250
Capital and scaleIndependent company
Credo AI
- Known funding
- $50M
- Operating scale
- Credo AI says its customers include Global 2000 and Fortune 500 enterprises
- Backing context
- Credo AI announced a $12.8 million Series A led by Sands Capital with participation from Decibel VC and AI Fund
Credo AI announced a $12.8 million Series A led by Sands Capital with participation from Decibel VC and AI Fund
Founders and leadership1 person listed
- Navrina SinghCurrent role listed
Founder & 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
- 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 sourceCredo AI company pageCredo AI says it was founded in 2020 and builds an AI governance platform.Company sourceCredo AI Series A announcementCredo AI announced a $12.8 million Series A led by Sands Capital with participation from Decibel VC and AI Fund.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 Credo AI as an enterprise AI governance and AI TRiSM platform, not as a replacement for SSE, browser data loss prevention (DLP), non-human identity (NHI), or endpoint controls.
- Public evidence supports AI and agent inventory, governance workflows, policy and regulatory mapping, continuous risk assessment, audit evidence, automated red teaming, and runtime trace evaluation.
- Public pages describe modular packaging but do not expose a per-user, per-system, or usage-based commercial 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 referenceCSA AI Controls Matrix
- Requirements with public support
- 13
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 12
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 12
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 12
- 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
Limited 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
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
No supporting claim foundDetect, 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
Limited 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
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
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
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
Limited public supportPublicly 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.
Credo AI claims its governance platform discovers and classifies shadow AI across the enterprise.
Shadow AI discovery and classification
Credo AI claims a centralized inventory of agents, models, applications, and shadow AI with enterprise auto-discovery.
Centralized inventory of every AI system, including agents, models, apps, and shadow AI, with auto-discovery across your enterprise.
Credo AI claims its AI Registry records governed systems, agents, models, and applications in a central inventory.
register every system in a central inventory
Credo AI claims policy enforcement and production monitoring, with planned enforcement integrations for CI/CD, cloud access security broker (CASB), and application programming interface (API) gateways.
Planned enforcement integration with CI/CD, CASBs, and API gateways
Credo AI materials reviewed did not provide a public claim for inline detection, redaction, or blocking of sensitive data in prompts, responses, files, retrieval, or memory.
No quoted source text is recorded for this claim.
Credo 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
Credo AI claims runtime observability and trace-level policy enforcement for governed AI systems.
Runtime observability with trace-level policy enforcement
Credo AI claims regulatory policy packs, governance workflows, approval gates, automated evidence generation, and audit trails.
Pre-built policy packs for EU AI Act, NIST AI RMF, ISO 42001, and SOC 2 with automated governance workflows and audit-ready evidence.
Credo AI claims automated red teaming and drift detection within its Risk Intelligence module.
Automated red-teaming and drift detection
Credo AI claims third-party model tracking, model and vendor lineage graphs, and model metadata in its agent governance inventory.
Model + vendor lineage graphs
Credo AI claims platform and Model Context Protocol (MCP) server governance while describing CI/CD, cloud access security broker (CASB), and application programming interface (API) gateway enforcement integration as planned.
Platform & MCP Server governance
Credo AI claims continuous evaluation of agent traces with escalation, monitoring, and alerts.
Continuous evaluation of agent traces to detect policy violations, drift, and unsafe behavior, with human-in-the-loop escalation.
Credo AI claims dependency mapping across agents, models, tools, and data, plus agentic risk coverage for inter-agent risk.
Dependency graph mapping across agents, models, tools, and data
Credo AI materials reviewed did not provide a public claim for service-account, workload-identity, application programming interface (API)-key, secret-rotation, or machine-credential lifecycle management.
No quoted source text is recorded for this claim.
Credo AI claims agent cards containing purpose, tools, data sources, and guardrails, with ownership and accountability in the Agent Registry.
Agent Registry with agent cards (purpose, tools, data sources, guardrails)
Credo 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.
Credo AI materials reviewed did not provide a public claim for model or agent spend attribution, budgets, rate limits, chargeback, or anomalous token-cost controls.
No quoted source text is recorded for this claim.
Credo AI claims modular product packaging whose modules can operate independently.
Each module works independently but is more powerful together.
Credo AI claims integrations across cloud, agent, GRC, DevOps, and MLOps platforms, positioning it as a governance layer over enterprise AI systems.
AWS, Azure, GCP, Databricks, Snowflake