Company context
Public-company subsidiary / AI security center under Check Point (NASDAQ: CHKP)
Check Point positions Lakera as the foundation for its Global Center of Excellence for AI Security
Vendor research
Lakera / Check Point
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
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.
- $2.7B annual revenue (2025-12-31)
- $30M known funding
- 51-200 employees
- Founded 2021
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
- 5
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.
Founded2021
HeadquartersDual-headquartered in Zurich, Switzerland and San Francisco, California
OwnershipPublic-company subsidiary / AI security center under Check Point (NASDAQ: CHKP)
Employees51-200
Capital and scaleIndependent company
Lakera
- Known funding
- $30M
- Current owner annual revenue
- $2.7B
- Operating scale
- Check Point positions Lakera as the foundation for its Global Center of Excellence for AI Security
- Backing context
- Formerly VC-backed; raised $20M Series A led by Atomico before acquisition
Series A · $20M · 2024-07-24
CHECK POINT SOFTWARE TECHNOLOGIES LTD (CHKP) · period ended 2025-12-31 · filed 2026-03-31
Named investors
Atomico · Citi Ventures · Dropbox Ventures · redalpine
Founders and leadership3 people listed
- David HaberStatus not confirmed
Co-Founder & CEO before acquisition
- Dr. Mateo Rojas-CarullaStatus not confirmed
Co-Founder
- Dr. Matthias KraftStatus not confirmed
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
- 2021
- Workforce scale
- 51-200
- 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 sourceCheck Point acquisition announcementCheck Point announced acquiring Lakera and described Lakera as founded in 2021 and dual-headquartered in Zurich and San Francisco.Company sourceLakera Series A announcementLakera announced a $20M Series A led by Atomico, bringing total funding to $30M before acquisition.Company information sourceLakera LinkedIn company profileSupports the company facts shown in this profile.Regulatory filing20-F annual filingCHECK POINT SOFTWARE TECHNOLOGIES LTD (CHKP) · period ended 2025-12-31
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
AI application runtime protectionCore product focusAI testing and adversarial assuranceCore product focusAction-taking agent safeguardsCore product focusEndpoint AI application controlsRelated coverageBrowser and extension controlsRelated coverageAI data protectionRelated coverageEmployee AI access and usage controlsRelated coverage
Buyer context
- Public-company ownership may improve procurement comfort for global buyers but changes roadmap diligence to Check Point integration.
- Particularly relevant for AI app and agent runtime security rather than workforce/software as a service (SaaS) discovery alone.
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
- 13
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 13
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 13
- 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.
- 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
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
Strong 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
Strong public supportApply 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
Limited 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
Limited 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
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.
Check Point AI Agent Security claims continuous agent inventory, holistic risk ratings, contributing-factor explanations, and risk mappings to OWASP and MITRE ATLAS.
Every discovered agent gets a holistic risk rating with the contributing factors explained.
Check Point AI Agent Security and Lakera Guard materials reviewed did not provide a public customer-facing product claim for automated adversarial testing, repeatable attack suites, or release-gate red teaming.
No quoted source text is recorded for this claim.
Check Point AI Agent Security claims posture detection for unofficial, unknown, or vulnerable Model Context Protocol (MCP) servers, untrusted components, suspicious tool code, and likely-malicious tools.
Flags unofficial, unknown, and vulnerable MCP servers, untrusted components, and suspicious or likely-malicious tool code.
Check Point AI Agent Security claims real-time Guard application programming interface (API) screening and enforcement across prompts, tool calls, tool responses, tool descriptions, data leakage, content violations, and off-policy agent behavior.
Real-time screening and flagging of prompt attacks, data leakage, content violations, and off-policy agent behavior through the Guard API.
Check Point AI Agent Security claims tool allow or deny controls and posture findings for missing authentication, static credentials, and execution under an author’s credentials.
Tool Allow/Deny List limits available actions.
Check Point AI Agent Security and Lakera Guard materials reviewed did not establish controls specifically for coding-agent commands, developer-workstation files or networks, integrated development environment (IDE) extensions, hooks, secrets, or package actions.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Lakera Workforce AI Security and Guard 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.
Lakera claims Workforce AI Security discovers shadow AI and stops sensitive data exposure across AI apps, browser extensions, desktop agents, integrated development environments (IDEs), and Model Context Protocol (MCP)-connected tools.
Discover shadow AI, assess risk, govern employee usage, and stop sensitive data exposure across AI apps, browser extensions, desktop agents, IDEs, and MCP-connected tools.
Lakera claims workforce AI visibility and policy enforcement across AI apps, browser extensions, desktop agents, integrated development environments (IDEs), Model Context Protocol (MCP)-connected tools, and software as a service (SaaS) services.
Employee AI usage is spreading faster than traditional controls can keep up — across browser tools, desktop apps, copilots, IDEs, and connected SaaS services.
Lakera claims employee AI usage includes approved and unapproved tools across browser, desktop, and software as a service (SaaS).
Teams are using sanctioned and unsanctioned AI tools across the browser, desktop, and SaaS.
Lakera claims security teams need policy by app, user, data type, and action for employee AI usage.
Security teams need policy by app, user, data type, and action — not blanket allow or block decisions.
Lakera claims Workforce AI Security stops sensitive data exposure across AI apps, browser extensions, desktop agents, integrated development environments (IDEs), and Model Context Protocol (MCP)-connected tools.
Discover shadow AI, assess risk, govern employee usage, and stop sensitive data exposure across AI apps, browser extensions, desktop agents, IDEs, and MCP-connected tools.
Lakera claims AI-agent landscape discovery, risk assessment, and real-time protection enforcement.
Discover your agent landscape, assess risk, and enforce protection in real time.
Lakera claims AI Agent Security provides visibility into agent usage and Model Context Protocol (MCP)-connected systems, adjacent to agent-to-tool security.
AI Agent Security provides visibility into agent usage and MCP-connected systems across your environment, including agents your teams did not explicitly build or register.
No public claim found for this capability.
No quoted source text is recorded for this claim.
Lakera claims Workforce AI Security discovers shadow AI and governs employee usage across AI apps, browser extensions, desktop agents, integrated development environments (IDEs), and Model Context Protocol (MCP)-connected tools.
Discover shadow AI, assess risk, govern employee usage, and stop sensitive data exposure across AI apps, browser extensions, desktop agents, IDEs, and MCP-connected tools.
Lakera claims it secures AI agents from discovery to runtime and enforces protection in real time.
Secure AI Agents from Discovery to Runtime
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.