ASAI Security ResearchIndependent public-source research
Public reviewread only

Public-source research

Vendor evidence

Review what vendors say publicly, the exact quoted source text, the security requirement each statement may support, and what still needs verification.

Research library coverageCounts describe the research workflow, not vendor quality or product effectiveness.View details
Evidence records1280
Source checked863
Needs verification0
Source captured0
No supporting claim found411
Excluded from evidence6

Showing 1–19 of 19 evidence records

Clear all filters
Lakera / Check PointAI governance, risk, and complianceSource checkedLimited public support for this requirement

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.
Related framework references (5)
Lakera / Check PointAI assurance and adversarial testingNo supporting claim found

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.
Related framework references (5)
Lakera / Check PointAI model and supply-chain securitySource checkedLimited public support for this requirement

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.
Related framework references (5)
Lakera / Check PointAI gateway, tool-connection, and runtime controlsSource checkedStrong public support for this requirement

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.
Related framework references (5)
Lakera / Check PointAI agent identity and permissionsSource checkedLimited public support for this requirement

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.
Related framework references (5)
Lakera / Check PointAI coding-agent and workstation securityNo supporting claim found

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.
Related framework references (5)
Lakera / Check PointAI cost and usage controlsNo supporting claim found

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.
Related framework references (5)
Lakera / Check PointUnapproved AI use discoverySource checkedStrong public support for this requirement

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.
Related framework references (5)
Lakera / Check PointAI-feature discovery in business applicationsSource checkedStrong public support for this requirement

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.
Related framework references (5)
Lakera / Check PointApproved AI usage monitoringSource checkedLimited public support for this requirement

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.
Related framework references (5)
Lakera / Check PointControls for unapproved AI useSource checkedStrong public support for this requirement

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.
Related framework references (5)
Lakera / Check PointSensitive-data protection for generative AISource checkedStrong public support for this requirement

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.
Related framework references (5)
Lakera / Check PointAction-taking agent monitoringSource checkedStrong public support for this requirement

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.
Related framework references (5)
Lakera / Check PointAgent-to-agent communication securitySource checkedRelated public context only

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.
Related framework references (5)
Lakera / Check PointBrowser and business-application controlsSource checkedStrong public support for this requirement

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.
Related framework references (5)