No solution approach in the current research connects this vendor to this use case.
Vendor research
Cequence AI Gateway
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
No related approach foundBack 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.
?
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.
- $100M known funding
- Founded 2014
- Employee scale not sourced
- Private-company revenue and profitability not sourced
Company context
Private independent, venture-backed company; no acquisition or parent-company claim was found on the reviewed Cequence-controlled pages
Cequence says it protects more than 10B daily application programming interface (API) interactions and 4B user accounts; its April 2026 Agent Personas announcement cited more than 140 verified enterprise application integrations
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 11
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 8
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.
Founded2014 on Cequence's current company page; some older Cequence-controlled materials state 2015
HeadquartersSanta Clara, California
OwnershipPrivate independent, venture-backed company; no acquisition or parent-company claim was found on the reviewed Cequence-controlled pages
EmployeesNot yet sourced
Capital and scaleIndependent company
Cequence Security
- Known funding
- $100M
- Operating scale
- Cequence says it protects more than 10B daily API interactions and 4B user accounts; its April 2026 Agent Personas announcement cited more than 140 verified enterprise application integrations
- Backing context
- Cequence reported more than $100M in total funding in 2023; its $60M Series C was led by Menlo Ventures with participation from ICON Ventures, Telstra Ventures, HarbourVest Partners, Shasta Ventures, Dell Technologies Capital, and T-Mobile Ventures
Cequence reported more than $100M in total funding in 2023; its $60M Series C was led by Menlo Ventures with participation from ICON Ventures, Telstra Ventures, HarbourVest Partners, Shasta Ventures, Dell Technologies Capital, and T-Mobile Ventures
Named investors
Prosperity7 Ventures · Dell Technologies Capital · HarbourVest Partners · ICON Ventures · Menlo Ventures · Shasta Ventures · Telstra Ventures · T-Mobile Ventures
Founders and leadership2 people listed
- Ameya TalwalkarCurrent role listed
President, CEO, and Co-Founder
- Shreyans MehtaCurrent role listed
CTO and Co-Founder
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
- 2014 on Cequence's current company page; some older Cequence-controlled materials state 2015
- Workforce scale
- Not yet sourced
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- Current employee range is not yet supported by a public source.
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 sourceCequence AI Gateway product pageCequence describes MCP and direct API proxying, trusted MCP and API registries, OAuth integration, agent identities and permissions, runtime guardrails, audit logging, sensitive-data controls, and AI discovery.Company sourceCequence Agent Personas announcementCequence announced general availability of Agent Personas on April 28, 2026, with infrastructure-level privilege scope down to individual tool calls.Company sourceCequence company pageCequence's current company page provides its founder history, Santa Clara address, leadership, investor list, and operating-scale statements.Company sourceCequence Prosperity7 investment announcementCequence announced a $60M Series C in December 2021, bringing total investment to $100M, and later reported more than $100M in total funding after an additional 2023 investment.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat Cequence AI Gateway as an agentic security and governance product rooted in application programming interface (API) access, not as a generic application programming interface (API)-security record or an SSE control plane.
- Public evidence is strongest for Model Context Protocol (MCP) and application programming interface (API) mediation, trusted endpoint registries, OAuth-based access, tool-scoped agent permissions, behavioral controls, auditability, and sensitive-data enforcement.
- Confirm agent identity lifecycle depth during diligence: public pages establish agent-to-permission mapping, token lifecycle, tool-level scope, and containment, but do not establish a full generic non-human identity (NHI) inventory and credential-rotation program.
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
- 10
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 10
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 10
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 10
- 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.
- 01Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 02Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 03Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 04Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 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
No supporting claim foundDiscover 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
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
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
No supporting claim foundInventory 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
No supporting claim foundDiscover, 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
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
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
Limited 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.
Cequence AI Gateway materials reviewed did not provide a public claim for workforce discovery of unmanaged AI tools, accounts, users, and usage.
No quoted source text is recorded for this claim.
Cequence AI Gateway materials reviewed did not provide a public claim for AI-feature discovery across the enterprise business-application environment.
No quoted source text is recorded for this claim.
Cequence claims real-time AI-tool traffic visibility and audit logging of user, agent, tool, application, and application programming interface (API) behavior.
Real-time visibility into AI-tool traffic with full audit logging
Cequence claims behavioral boundaries and inline policy that prevent agent overreach.
prevent agent overreach
Cequence claims data loss prevention (DLP) scanning, monitoring, redaction, and blocking across AI-agent requests and Model Context Protocol (MCP) responses.
Apply DLP scanning to AI agent requests and MCP server responses
Cequence AI Gateway materials reviewed did not provide a public claim for session-level browser or software as a service (SaaS) controls for employee AI use.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Cequence claims real-time guardrails that block prompt injection and business-logic abuse.
block prompt injections and business logic abuse
Cequence AI Gateway materials reviewed did not provide a public claim for AI inventory, risk, approval, exception, and compliance workflows.
No quoted source text is recorded for this claim.
Cequence AI Gateway materials reviewed did not provide a public claim for AI-specific adversarial testing and release assurance.
No quoted source text is recorded for this claim.
Cequence AI Gateway materials reviewed did not provide a public claim for model and AI-artifact supply-chain inspection.
No quoted source text is recorded for this claim.
Cequence claims inline policy enforcement for every tool call across a full agent session.
enforces policy inline in the request path
Cequence claims detailed runtime tracking of user, agent, tool, application, and application programming interface (API)-call behavior.
detailed tracking of user, agent, and tool behavior
Cequence claims authentication and inline authorization of agent-to-tool and agent-to-application programming interface (API) handoffs.
full session, on every tool call
Cequence claims identity-based agent access with built-in token lifecycle management.
Built-in token lifecycle management
Cequence claims agent authentication followed by verification of every action and tool-scoped Agent Persona permissions.
AI Gateway authenticates agents and then verifies every action taken.
Cequence claims a governed application programming interface (API) registry for code-writing agents that build against application programming interface (API) specifications.
Code-writing agents build against API specs.
Cequence AI Gateway materials reviewed did not provide a public claim for AI usage attribution, budgeting, anomaly detection, and cost controls.
No quoted source text is recorded for this claim.
Cequence AI Gateway materials reviewed did not provide a public claim for a public per-user, per-app, usage-based, or enterprise-platform commercial model.
No quoted source text is recorded for this claim.
Cequence positions AI Gateway as an external proxy and policy layer for Model Context Protocol (MCP) and direct application programming interface (API) calls from agents.
Proxy MCP tool calls