AI API and web application protectionAdditional if: System uses APIs, tool servers, or web apps · The system exposes or uses APIs, model endpoints, tool servers, web applications, or agent-to-tool traffic that application or API security controls can inspect.AI application runtime protectionDirectly addresses · Protect custom AI applications, information-retrieval systems, model calls, prompts, and outputs while they run.AI data protectionAdditional if: Sensitive content enters AI flows · Sensitive content must be inspected or blocked in prompts, responses, files, retrieved information, or tool calls.
Vendor research
Harness AI Security / Traceable
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
Established?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity signal, not a product-quality rating.
?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity 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.
- $570M known funding
- 1,200+ employees
- Founded 2017
- Private-company revenue and profitability not sourced
Company context
Private Harness product line following the completed Harness-Traceable merger
Harness says it has 1,200+ employees and 1,000+ customers; its 2026 modern-slavery statement reported 1,248 direct employees as of January 31, 2026
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 6
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 13
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.
FoundedHarness launched in 2017; Harness says Traceable was co-founded in 2018; the companies completed their merger on March 4, 2025
HeadquartersSan Francisco, California
OwnershipPrivate Harness product line following the completed Harness-Traceable merger
Employees1,200+
Capital and scaleOwned business
Harness, Inc.
- Known funding
- $570M
- Operating scale
- Harness says it has 1,200+ employees and 1,000+ customers; its 2026 modern-slavery statement reported 1,248 direct employees as of January 31, 2026
- Backing context
- Harness says it has raised $570M in venture capital from its listed investors
Harness says it has raised $570M in venture capital from its listed investors
Named investors
Menlo Ventures · IVP · Unusual Ventures · Citi Ventures
Founders and leadership2 people listed
- Jyoti BansalCurrent role listed
Co-Founder and CEO
- Rishi SinghCurrent role listed
Co-Founder and Board Member
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
- Harness launched in 2017; Harness says Traceable was co-founded in 2018; the companies completed their merger on March 4, 2025
- Workforce scale
- 1,200+
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
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 sourceHarness AI Security product pageHarness describes discovery of LLMs, MCP servers and tools, AI APIs, and third-party GenAI services, along with AI-specific testing, sensitive-data inspection, guardrails, and runtime protection.Company sourceHarness-Traceable merger announcementHarness announced that its merger with Traceable was completed effective March 4, 2025.Company sourceHarness company pageHarness lists its 2017 San Francisco launch, employee and customer scale, funding, investors, leadership, offices, and Traceable co-founder context.Company sourceHarness modern-slavery statementHarness identifies itself as a privately held Delaware corporation headquartered in San Francisco and reports its direct employee count as of January 31, 2026.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat Harness AI Security as the current product identity and retain Traceable as lineage and an alias; do not create a second target-vendor record for the former standalone company.
- Public product evidence is strongest for AI asset discovery, technical risk scoring, AI-specific application testing, sensitive-data inspection, guardrails, and runtime attack blocking.
- Confirm packaging and deployment boundaries during diligence, particularly whether Model Context Protocol (MCP) traffic is only discovered and assessed or is also mediated through a tool-level allow-or-deny enforcement point.
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
- 5
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 5
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 5
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 1
- 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.
- 03Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 04Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
- 05Generative AI application security
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
- 06Generative AI application security
A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
- 07AI assurance and adversarial testing
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
- 08AI 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
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
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
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
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
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
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
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
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
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
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
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.
Harness AI Security 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.
Harness AI Security 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.
Harness claims continuous monitoring of AI application programming interface (API) traffic and an inventory of large language model (LLM), Model Context Protocol (MCP), AI application programming interface (API), and generative AI assets.
Automatically monitor all AI API traffic
Harness AI Security materials reviewed did not provide a public claim for blocking, coaching, redirecting, or containing unapproved AI use.
No quoted source text is recorded for this claim.
Harness claims inspection of AI prompts and responses for PII and other sensitive data.
inspect AI prompts and responses for sensitive data types
Harness AI Security 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
Harness claims real-time detection and blocking of prompt-injection attacks against running AI applications.
Detect and block prompt injection attacks in real time
Harness AI Security 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.
Harness claims dynamic predeployment testing of AI-native applications for prompt injection, data exfiltration, and other AI-specific threats.
Dynamically test your AI-native applications for AI-specific threats
Harness AI Security 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.
Harness claims runtime AI guardrail policies and blocking for protected AI applications.
Create policies to enforce best practices for AI usage
Harness AI Security materials reviewed did not provide a public claim for runtime visibility into agent decisions, actions, tools, and outcomes.
No quoted source text is recorded for this claim.
Harness AI Security materials reviewed did not provide a public claim for authorization and control across agent, tool, Model Context Protocol (MCP), or connector handoffs.
No quoted source text is recorded for this claim.
Harness AI Security materials reviewed did not provide a public claim for non-human identity and machine-credential lifecycle controls.
No quoted source text is recorded for this claim.
Harness AI Security materials reviewed did not provide a public claim for agent registration, delegated authorization, scoped access, and revocation.
No quoted source text is recorded for this claim.
Harness AI Security materials reviewed did not provide a public claim for coding-agent, integrated development environment (IDE), CLI, workstation, tool, and package-action governance.
No quoted source text is recorded for this claim.
Harness AI Security 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.
Harness AI Security 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.
Harness positions AI Security as an external AI discovery, testing, and protection layer built on runtime application programming interface (API) visibility.
built on our industry-leading API security platform