Company context
Private, VC-backed
Runtime application protection vendor extending into AI, Model Context Protocol (MCP) Gateway, application programming interface (API), and cloud protection
Vendor research
Operant 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
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.
- $13.5M known funding
- <50 employees
- Founded 2021
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.
Founded2018
HeadquartersSan Francisco, California
OwnershipPrivate, VC-backed
Employees<50
Capital and scaleIndependent company
Operant AI
- Known funding
- $13.5M
- Operating scale
- Runtime application protection vendor extending into AI, MCP Gateway, API, and cloud protection
- Backing context
- $10M Series A co-led by SineWave Ventures and Felicis; total funding reported at $13.5M
Series A · $10M · 2024-09-12
Named investors
SineWave Ventures · Felicis · Alumni Ventures · Massive · Calm Ventures · Gaingels
Founders and leadership3 people listed
- Vrajesh BhavsarCurrent role listed
Co-Founder & CEO
- Priyanka TembeyCurrent role listed
Co-Founder & CTO
- Ashley RoofCurrent role listed
Co-Founder & COO
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
- 2018
- Workforce scale
- <50
- 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 limits5 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 sourceOperant AI Series A announcementOperant announced a $10M Series A and listed San Francisco as headquarters.Company sourceOperant AI contact pageOperant lists its San Francisco office address on the contact page.Company information sourceStored company websiteSupports the company facts shown in this profile.Company information sourceOperant AI company and founder pageSupports the company facts shown in this profile.Company information sourceOperant AI Series A announcementSupports 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
- Relevant if the client needs runtime protection for custom AI apps and application programming interfaces (APIs), not only workforce AI governance.
- Buyer diligence should compare against web application and application programming interface protection (WAAP)/application programming interface (API) security, CNAPP, and AppSec controls already in place.
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
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- Requirements with public support
- 15
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 15
- Related requirements
- 13
- References
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- 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
Limited 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
Limited 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
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
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
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
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
Strong 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.
Operant claims a centralized AI and Model Context Protocol (MCP) registry, security graph, threat mappings, agent and tool governance, audit artifacts, regulatory-control mappings, endpoint inventory, and real-time policy across the AI surface.
Establish a centralized governance framework for managing AI agents and tools across the enterprise.
Operant claims open-source Woodpecker red teaming for early vulnerability detection and attack simulation as part of its AI security approach.
Operant’s Woodpecker simplifies this with open-source red teaming, enabling early vulnerability detection.
Operant claims an enterprise Model Context Protocol (MCP) and skills registry, trust scoring, trust zones, artifact quarantine, component and relationship mapping, and blocking of untrusted servers, tools, skills, plugins, packages, and supply-chain behavior.
Map trust scores and enforce trust boundaries, ensuring only verified entities operate within your AI agent supply chain.
Operant claims an large language model (LLM) Gateway and Model Context Protocol (MCP) Gateway with routing, request firewall, prompt and response inspection, tool-call blocking, intent guards, data loss prevention (DLP), redaction, threat detection, rate limits, and runtime response.
Tool-call inspection and blocking with full AuthNZ enforcement.
Operant claims AI non-human identity detection, agent identity and access control, fine-grained identity-aware Model Context Protocol (MCP) enforcement, least privilege, authorization validation, trust scores, and per-agent tool restrictions.
Detect and block unauthenticated and unauthorized AI agent behavior in real time.
Operant claims device-layer controls for Claude Code, Cursor, Copilot, Cline, Windsurf, Aider, and custom agents, covering shell commands, files, repositories, credentials, packages, plugins, skills, prompts, Model Context Protocol (MCP) tools, application programming interfaces (APIs), and proprietary code.
Real-time shell command monitoring distinguishes legitimate developer tooling from injection-driven attacks — and blocks before execution.
Show 13 additional evidence records
Operant AI platform materials reviewed did not provide a public product claim for AI usage-cost attribution, token or spend metrics, budgets, chargeback, or cost-aware model routing.
No quoted source text is recorded for this claim.
Operant claims real-time visibility and controls for every Model Context Protocol (MCP) server, client, tool, and connection in the environment, including registries, allow/block lists, and non-human identity (NHI) access controls.
Real-time visibility and controls for every MCP server, client, tools, and connections in your environment, with MCP registries, white list/black lists, and NHI access controls, so your entire team can develop AI and Agents, safer and faster.
Operant claims it can discover managed and unmanaged agents running in software as a service (SaaS) platforms.
Discover managed and unmanaged agents running in cloud environments, SaaS platforms, and development tools.
Operant AI claims it secures AI prompts, interactions, agents, and all data-in-use through the live application stack.
Operant actively secures AI prompts, interactions, agents, and all data-in-use as it flows through your live application stack
Operant AI claims inline controls that block rogue agents, out-of-scope actions, unauthorized access, and critical agentic attacks.
blocking out-of-scope actions before execution and alerting your security team with full action traces before damage is done.
Operant AI claims it auto-redacts sensitive data inline across AI prompts, interactions, agents, and data-in-use within the live application stack.
Proactively block critical LLM and GenAI threats like prompt injection and data exfiltration while maintaining full data privacy and data governance with Inline Auto-Redaction of Sensitive Data.
Operant claims real-time protection across the full agent toolchain from Model Context Protocol (MCP) clients and endpoints to live, interactive agentic applications.
Operant’s real-time protection across the full agent toolchain — from MCP clients and endpoints to live, interactive agentic applications — lets technology leaders move fast without compromising customer privacy
Operant AI claims real-time protection across the agent toolchain, including Model Context Protocol (MCP) clients, endpoints, and live interactive agentic applications.
Operant’s real-time protection across the full agent toolchain — from MCP clients and endpoints to live, interactive agentic applications — lets technology leaders move fast without compromising customer privacy
Operant AI claims Agent Protector catalogs agent identities, including user and service accounts, and its Model Context Protocol (MCP) security includes non-human identity (NHI) access controls.
The system creates detailed catalogs of agent identities, including both user and service accounts
No public claim found for this capability.
No quoted source text is recorded for this claim.
Operant AI claims it detects and prioritizes AI-specific risks including prompt injection, large language model (LLM) poisoning, model theft, and sensitive data leakage.
Detect and prioritize AI-specific risks like prompt injection, LLM poisoning, model theft, and sensitive data leakage.
No public claim found for this capability.
No quoted source text is recorded for this claim.
Operant AI claims AI Gatekeeper provides real-time security for AI applications and agents.
AI Gatekeeper Real-time Security for AI Applications and Agents