Vendor research
Orca AI-SPM
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
Scaled?
ScaledA private provider with at least $100M in known funding or at least 250 employees.This is a company-scale signal, not a product-quality rating.
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ScaledA private provider with at least $100M in known funding or at least 250 employees.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.
- $632M known funding
- 250-1000 employees
- Founded 2019
Company context
Private independent company; reviewed Orca Security-controlled sources do not identify an acquirer or parent company
Orca says hundreds of organizations use its agentless multi-cloud security platform
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 9
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 10
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.
Founded2019
HeadquartersPortland, Oregon
OwnershipPrivate independent company; reviewed Orca Security-controlled sources do not identify an acquirer or parent company
Employees250-1000
Capital and scaleIndependent company
Orca Security
- Known funding
- $632M
- Operating scale
- Orca says hundreds of organizations use its agentless multi-cloud security platform
- Backing context
- Orca says it has raised nearly $630 million and was valued at $1.8 billion, backed by Temasek, CapitalG, ICONIQ, Redpoint, and others
Orca says it has raised nearly $630 million and was valued at $1.8 billion, backed by Temasek, CapitalG, ICONIQ, Redpoint, and others
Founders and leadership2 people listed
- Gil GeronCurrent role listed
Co-Founder & CEO
- Avi ShuaCurrent role listed
Co-Founder & Chief Innovation Officer
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
- 2019
- Workforce scale
- 250-1000
- Hiring activity
- 1 open positions · stable
A hiring count is shown only when a clickable source is available.
Ashby careers board ↗Core company facts have supporting public sources.
Company sources and research limits4 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 sourceOrca AI-SPM pageOrca describes agentless cloud AI model and package inventory, sensitive-data detection, access-key exposure, IAM, and configuration posture.Company sourceOrca about pageOrca provides its funding, valuation, investors, founders, and company history.Company information sourceStored company websiteSupports the company facts shown in this profile.Company information sourceAshby careers boardSupports 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 Orca AI-SPM as the AI posture and bill-of-materials layer within Orca's broader CNAPP, not as a workforce AI, browser, agent gateway, or identity product.
- Public evidence supports cloud shadow AI, managed and unmanaged model inventory, a model and package BOM, training-data sensitivity, exposed AI keys, identity and access management (IAM) and configuration posture, and cloud attack-path context.
- Public pages reviewed did not establish software as a service (SaaS) embedded-AI discovery, browser controls, AI red teaming, Model Context Protocol (MCP) enforcement, agent identity or agent-to-agent (A2A) security, coding-agent controls, or AI FinOps.
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
- 8
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 7
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 7
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 7
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 7
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 7
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 7
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 7
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 7
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 7
- 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.
- 03Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 04Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 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
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
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
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
No supporting claim foundBlock, 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
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
No supporting claim foundTest 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
No supporting claim foundMediate 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
No supporting claim foundObserve 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
Limited 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
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
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.
Orca claims continuous discovery of managed, unmanaged, and shadow AI models across the entire cloud environment.
including any shadow AI
Orca materials reviewed did not provide a public claim for tenant-level inventory of embedded AI features across enterprise software as a service (SaaS) applications.
No quoted source text is recorded for this claim.
Orca claims a complete inventory and bill of materials for managed and unmanaged AI models and packages in AWS, Azure, and Google Cloud.
complete AI inventory and Bill of Materials (BOM)
Orca materials reviewed did not provide a public claim for workforce allow, coach, restrict, isolate, redirect, or block controls over unapproved AI use.
No quoted source text is recorded for this claim.
Orca claims detection of sensitive information in AI models and training data to prevent unintended exposure.
training data contain sensitive information
Orca materials reviewed did not provide a public claim for browser session controls over AI upload, download, copy, paste, sharing, or form submission.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Orca claims continuous detection of AI misconfigurations, exposed models, vulnerable packages, sensitive data, identity and access management (IAM) risks, and malware in cloud AI environments.
covering network security, data protection, access controls, and IAM
Orca claims ongoing AI compliance monitoring and a configuration-practices framework covering network, data, access, and identity and access management (IAM) settings.
AI Best Practices compliance framework
Orca materials reviewed did not provide a public product claim for adversarial AI red teaming, evaluation campaigns, or regression release gates.
No quoted source text is recorded for this claim.
Orca claims an AI bill of materials covering models and more than 50 software packages plus detection of vulnerable packages, editable training data, and exposed keys.
50+ AI models and software packages
Orca materials reviewed did not provide a public claim for an AI or Model Context Protocol (MCP) gateway that proxies and enforces prompt, response, tool-call, or Model Context Protocol (MCP) policy at runtime.
No quoted source text is recorded for this claim.
Orca materials reviewed did not provide a public claim for agent identities, tool calls, Model Context Protocol (MCP) actions, delegation, or agent workflow telemetry.
No quoted source text is recorded for this claim.
Orca materials reviewed did not provide a public claim for authenticating, authorizing, logging, or enforcing agent-to-agent communication.
No quoted source text is recorded for this claim.
Orca claims detection of exposed keys and tokens for AI services and software packages in code repositories.
keys and tokens to AI services and software packages
Orca materials reviewed did not provide a public claim for agent registration, ownership, delegated authorization, short-lived credentials, or agent lifecycle.
No quoted source text is recorded for this claim.
Orca materials reviewed did not provide a public claim for coding-agent commands, filesystem or network actions, skills, hooks, extensions, packages, or workstation activity.
No quoted source text is recorded for this claim.
Orca 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.
Orca positions AI-SPM as integrated into the Orca Cloud Security Platform rather than a separate point solution.
no point solutions needed
Orca claims AI-SPM within a unified agentless CNAPP covering AWS, Azure, Google Cloud, cloud assets, identities, data, vulnerabilities, and attack paths.
Orca Cloud Security Platform