Action-taking agent safeguardsDirectly addresses · Observe and govern agent plans, memory, tool use, delegated tasks, and actions while the agent runs.AI application runtime protectionDirectly addresses · Protect custom AI applications, information-retrieval systems, model calls, prompts, and outputs while they run.Coding-agent and developer workstation securityAdditional if: Coding agents reach developer systems · Coding agents can reach code editors, command-line tools, repositories, build systems, software packages, or developer workstations.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.AI gateway and tool-connection controlsAdditional if: Model or tool traffic uses a security gateway · Model, tool, connector, API, or Model Context Protocol (MCP) traffic is routed through a security gateway.
Vendor research
Knostic
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
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.
- $14M known funding
- 11-50 employees
- Founded 2023
- Private-company revenue and profitability not sourced
Company context
Private independent company; no acquisition or parent-company claim found on reviewed Knostic-controlled pages
Knostic positions its platform across AI agents, coding assistants, Model Context Protocol (MCP) servers, integrated development environment (IDE) extensions, citizen-coder applications, OpenClaw, and large language model (LLM)-powered vulnerability discovery
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 13
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 6
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.
Founded2023
HeadquartersHerndon, Virginia
OwnershipPrivate independent company; no acquisition or parent-company claim found on reviewed Knostic-controlled pages
Employees11-50
Capital and scaleIndependent company
Knostic
- Known funding
- $14M
- Operating scale
- Knostic positions its platform across AI agents, coding assistants, MCP servers, IDE extensions, citizen-coder applications, OpenClaw, and LLM-powered vulnerability discovery
- Backing context
- $11M seed led by Bright Pixel Capital; $14M total reported, with DNX Ventures, Seedcamp, and Silicon Valley CISO Investments participating
Seed · $11M · 2025-03-05
Named investors
Bright Pixel Capital · DNX Ventures · Seedcamp · Silicon Valley CISO Investments
Founders and leadership2 people listed
- Gadi EvronCurrent role listed
Co-Founder & CEO
- Sounil YuCurrent role listed
Co-Founder & CTO
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
- 2023
- Workforce scale
- 11-50
- 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 sourceKnostic homepageKnostic says it discovers and secures AI agents, coding assistants, and associated supply-chain risks including MCP servers, skills, IDE extensions, and rules.Company sourceKnostic about pageKnostic describes visibility for Copilot and other LLM-based enterprise AI tools and lists its Herndon office address.Company sourceSecurityWeek funding profileSecurityWeek reported that Knostic was founded by Gadi Evron and Sounil Yu and had raised $14M after an $11M seed round.Company information sourceKnostic LinkedIn company profileSupports 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.
Action-taking agent safeguardsCore product focusAI application runtime protectionCore product focusCoding-agent and developer workstation securityCore product focusAI asset and configuration securityCore product focusAI data protectionRelated coverageAI gateway and tool-connection controlsRelated coverage
Buyer context
- Treat Knostic as an agentic and AI coding-assistant security specialist with evidence around integrated development environment (IDE), Model Context Protocol (MCP), extension, secret/PII, and AI-SDLC controls.
- Public evidence supports shadow-AI visibility, approved Copilot/enterprise-AI visibility, data-exfiltration controls, agent discovery, Model Context Protocol (MCP)/tooling supply-chain visibility, and large language model (LLM)-powered vulnerability discovery.
- Public pages reviewed did not expose software as a service (SaaS) embedded-AI inventory, browser/session controls, non-human identity (NHI) lifecycle, AI FinOps, or pricing model claims.
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
- 12
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 12
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 12
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 2
- 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.
- 05Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 06Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 07Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 08Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
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
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
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
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
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
Limited 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.
Knostic claims need-to-know policy capture, knowledge classification, oversharing discovery, policy tuning, control validation, traceable decisions, audit reporting, and consistent enforcement across enterprise large language models (LLMs) and agents.
Explicitly captures and manages per-user, per-topic need-to-know policies.
Knostic and AgentMesh materials reviewed did not provide a public product claim for automated adversarial testing of customer models or agents, repeatable attack suites, or release-gate red teaming.
No quoted source text is recorded for this claim.
Knostic AgentMesh claims continuous discovery, version tracking, SHA-256 artifact identification, static-code scanning, and risk analysis for AI agent skills, Model Context Protocol (MCP) servers, integrated development environment (IDE) extensions, Claude Code plugins, and commands.
Continuously discovers, tracks, and scans AI agent skills, MCP servers, and IDE extensions for prompt injection and supply chain threats.
Knostic Kirin claims runtime monitoring, prompt-injection blocking, unsafe-action prevention, least-privilege policy, and consistent guardrail enforcement across software as a service (SaaS), in-house, and Model Context Protocol (MCP) agents.
Track every agentic action and stop unsafe behavior in real time.
Knostic claims OAuth-based agent authentication, agent-role to privilege mapping, least-privilege and need-to-know boundaries, and policy enforcement across software as a service (SaaS), in-house, and Model Context Protocol (MCP) agents.
Map agent roles to privileges and enforce need-to-know boundaries.
Knostic claims endpoint guardrails plus AgentMesh scanning for coding assistants, Claude and Cursor skills, Model Context Protocol (MCP) servers, integrated development environment (IDE) extensions, plugins, commands, code injection, cryptomining, reverse shells, and other agentic supply-chain threats.
Scan skills, MCP servers, and extensions before they touch your agents.
Show 13 additional evidence records
Knostic claims Shadow AI Spotlight provides visibility into unapproved AI tools usage.
Shadow AI Spotlight Visibility into unsanctioned AI tools usage
Knostic materials reviewed did not provide a public claim for software as a service (SaaS) AI inventory, embedded software as a service (SaaS) AI features, or third-party software as a service (SaaS) AI provider monitoring.
No quoted source text is recorded for this claim.
Knostic claims comprehensive visibility for Copilot and other large language model (LLM)-based enterprise AI tools to identify oversharing, undersharing, and inference risks.
Knostic provides comprehensive visibility for Copilot and other LLM-based Enterprise AI tools, enabling enterprises to identify potential oversharing, undersharing and inference risks.
Knostic claims it detects shadow AI, blocks data exfiltration, and stops destructive commands.
We detect shadow AI, block data exfiltration, and stop destructive commands like rm -rf .
Knostic claims OpenClaw controls for secret leaks, PII exposure, inbound secrets, and file-read operations.
Blocks destructive commands Redacts secrets and API keys Prevents PII exposure Logs and flags inbound secrets Gates exec and file-read operations
Knostic materials reviewed did not provide a public claim for browser extension enforcement, enterprise browser controls, software as a service (SaaS)-session controls, or identity-aware software as a service (SaaS) policy.
No quoted source text is recorded for this claim.
Knostic claims OpenAnt provides large language model (LLM)-powered vulnerability discovery for CI/CD pipelines with two-stage verification.
LLM-powered vulnerability discovery for CI/CD pipelines. Two-stage verification: Stage 1 detects, Stage 2 attacks - what survives is real
Knostic claims discovery, detection and response, inventory, supply-chain, and posture management for coding agents and Model Context Protocol (MCP).
Agent discovery (Cursor, Claude, etc.) Detection & Response Inventory / Supply chain Security Posture Management Reputation service
Knostic claims it secures AI agents, coding assistants, Model Context Protocol (MCP) servers, skills, integrated development environment (IDE) extensions, and rules as associated supply-chain risks.
Knostic discovers and secures AI agents and coding assistants, as well as associated supply chain risks, including MCP servers, skills, IDE extensions, and rules.
Knostic materials reviewed did not provide a public claim for non-human identity (NHI) ownership, service-account lifecycle, credential rotation, scoped credentials, or least-privilege governance.
No quoted source text is recorded for this claim.
Knostic materials reviewed did not provide a public claim for AI spend attribution, model cost routing, budget enforcement, rate limits, or runaway token controls.
No quoted source text is recorded for this claim.
Knostic materials reviewed did not provide a public per-user, per-seat, platform, usage-based, or enterprise pricing model.
No quoted source text is recorded for this claim.
Knostic claims it discovers and secures AI agents, coding assistants, and related supply-chain risks.
Knostic discovers and secures AI agents and coding assistants, as well as associated supply chain risks, including MCP servers, skills, IDE extensions, and rules.