Vendor research

Snyk Evo

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

Scaled
?

A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.

Company scale is separate from product features, effectiveness, and suitability.

$530M known funding
250-1000 employees

Company context

Private independent company

Snyk says it serves more than 4,800 global customers

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Research coverageCounts describe available public research, not product quality.View details

Vendor statements: 16 records
Source-checked records: 7
Evaluation requirements: 16 in this research model
Unresolved requirements: 11

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.

Founded2015

HeadquartersBoston, Massachusetts and London, United Kingdom

OwnershipPrivate independent company

Employees250-1000

Capital and scalePlatform provider

Snyk

Known funding: $530M
Operating scale: Snyk says it serves more than 4,800 global customers
Backing context: Privately funded; reviewed Evo product sources did not provide a current total-funding figure

Founders and leadershipMore research needed

Founder names and current roles are not yet supported by a public source in this research.

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: 2015
Workforce scale: 250-1000
Hiring activity: Not displayed

Open research questions

Founder names and current-company status are not yet supported by a public source.
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 sourceEvo by SnykSnyk describes Evo across agentic development security, AI-SPM, AI-BOM, generated-code validation, guardrails, and offensive testing.Company sourceSnyk Agent Security announcementSnyk's March 2026 announcement distinguishes generally available, open-preview, and private-preview components and states its customer scale.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.

Coding-agent and developer workstation securityCore product focus AI asset and configuration securityCore product focus AI testing and adversarial assuranceCore product focus AI application runtime protectionRelated coverage Action-taking agent safeguardsRelated coverage AI governance, risk, and complianceRelated coverage

Buyer context

Treat Evo as Snyk's AI-native security platform, with particular relevance to coding agents, code repositories, AI supply-chain components, and AI-native application testing.
Confirm component maturity during diligence because Evo AI-SPM is generally available while some agent guard and red-teaming functions are in preview.

Related frameworks

Where public vendor statements relate to framework requirements

11 related frameworks · expand when needed

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: 7
Related requirements: 11
References: 58

Requirements with public support: 7
Related requirements: 14
References: 71

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: 2
Related requirements: 3
References: 3

Review related requirements →

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.

01
Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
02
Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
03
Generative AI application security
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
04
Generative AI application security
A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
05
AI governance, risk, and compliance
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
06
AI governance, risk, and compliance
A policy, assessment, approval, exception, or remediation workflow changes the governed state of the test system.
07
AI assurance and adversarial testing
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
08
AI assurance and adversarial testing
Results include reproducible prompts or attack steps, affected component, severity, and remediation guidance.

Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand

Security requirementPublic supportRelated frameworksWhat to verify

Unapproved AI use discovery

Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.

Controls for unapproved AI use

Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Detect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

Sensitive prompt, response, or file test data is detected and classified during an AI interaction.

Browser and business-application controls

Apply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Inventory 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.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Test 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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Discover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Mediate 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.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

A test agent run captures plan, steps, tool calls, outcome, and timestamps.

Agent-to-agent communication security

Authorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Register 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.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Discover 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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Visibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.

Research incomplete

Commercial Metadata NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 ISO/IEC 42001 OWASP 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

Publicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.

Research incomplete

Commercial Metadata CSA AI Controls Matrix

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 16 source records. Open additional records only when needed.

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Unapproved AI use discoveryNo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for enterprise-wide discovery of unapproved AI use.

AI-feature discovery in business applicationsNo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for embedded AI discovery across the business-application environment.

Approved AI usage monitoringSource checkedLimited public support for this requirement

Snyk Evo claims discovery of AI assets in code and generation of a live AI bill of materials (AI-BOM).

Controls for unapproved AI useNo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for policy enforcement over unapproved AI use.

Sensitive-data protection for generative AINo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for inspection or enforcement over sensitive data moving through AI.

Browser and business-application controlsNo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for browser or software as a service (SaaS)-session controls for employee AI use.

Show 10 additional evidence records

Generative AI application securitySource checkedLimited public support for this requirement

Snyk Evo claims continuous security coverage across AI development and AI-native applications.

AI governance, risk, and complianceSource checkedLimited public support for this requirement

Snyk Evo claims enforceable AI policy across development and CI/CD workflows.

AI assurance and adversarial testingSource checkedStrong public support for this requirement

Snyk Evo claims continuous offensive testing that stress-tests applications and AI systems and validates exploitability.

AI model and supply-chain securitySource checkedStrong public support for this requirement

Snyk Evo claims discovery of models, agents, Model Context Protocol (MCP) servers, datasets, and plugins in repositories plus AI supply-chain security.

AI gateway, tool-connection, and runtime controlsNo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for inline model, agent, tool, application programming interface (API), or Model Context Protocol (MCP) policy enforcement.

Action-taking agent monitoringSource checkedStrong public support for this requirement

Snyk Evo claims visibility and governance over the tools, services, actions, and output of development agents.

Agent-to-agent communication securityNo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for trust or policy enforcement between agents.

Non-human identity and service-account securityNo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for non-human identity, service-account, secret, or workload credential lifecycle controls.

AI agent identity and permissionsNo supporting claim found

Snyk Evo materials reviewed did not provide a public claim for agent registration, delegated authorization, task-scoped access, and revocation.

AI coding-agent and workstation securitySource checkedStrong public support for this requirement

Snyk Evo claims real-time action guardrails and generated-code validation before code reaches repositories, pipelines, or production.

Snyk Evo

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.

Private independent company

Snyk

Where public vendor statements relate to framework requirements

Vendor statements and quoted evidence

Snyk Evo materials reviewed did not provide a public claim for enterprise-wide discovery of unapproved AI use.

Snyk Evo materials reviewed did not provide a public claim for embedded AI discovery across the business-application environment.

Snyk Evo claims discovery of AI assets in code and generation of a live AI bill of materials (AI-BOM).

Snyk Evo materials reviewed did not provide a public claim for policy enforcement over unapproved AI use.

Snyk Evo materials reviewed did not provide a public claim for inspection or enforcement over sensitive data moving through AI.

Snyk Evo materials reviewed did not provide a public claim for browser or software as a service (SaaS)-session controls for employee AI use.

Snyk Evo claims continuous security coverage across AI development and AI-native applications.

Snyk Evo claims enforceable AI policy across development and CI/CD workflows.

Snyk Evo claims continuous offensive testing that stress-tests applications and AI systems and validates exploitability.

Snyk Evo claims discovery of models, agents, Model Context Protocol (MCP) servers, datasets, and plugins in repositories plus AI supply-chain security.

Snyk Evo materials reviewed did not provide a public claim for inline model, agent, tool, application programming interface (API), or Model Context Protocol (MCP) policy enforcement.

Snyk Evo claims visibility and governance over the tools, services, actions, and output of development agents.

Snyk Evo materials reviewed did not provide a public claim for trust or policy enforcement between agents.

Snyk Evo materials reviewed did not provide a public claim for non-human identity, service-account, secret, or workload credential lifecycle controls.

Snyk Evo materials reviewed did not provide a public claim for agent registration, delegated authorization, task-scoped access, and revocation.

Snyk Evo claims real-time action guardrails and generated-code validation before code reaches repositories, pipelines, or production.

Scaled
?