Enterprises adopting AI are seeing risk spread across multiple layers, not just in one system or configuration. Check Point focuses on three main challenge areas:

• Employee AI usage: Staff use hundreds of AI tools across browsers, desktops, and IDEs. This creates “Shadow AI” that security teams can’t easily see or govern, increasing the chance of data leakage and misuse.
• AI applications: AI apps generate unpredictable outputs in real time. This can lead to harmful or non-compliant content, prompt injection attacks, and data exfiltration if not monitored and controlled.
• AI agents: Autonomous agents act on human authority and connect to corporate systems and tools. Without proper guardrails, they can make unsafe decisions, abuse privileges, or create risky autonomy loops.

Check Point’s AI Defense Plane is designed as a single control plane to manage these risks across the enterprise. It provides:

• Unified visibility into who is using which AI tools, how they are used, and what data is involved.
• Inline protection where AI operates (browsers, applications, agents), inspecting prompts, outputs, and actions in real time.
• Centralized governance across people, apps, and agents, so AI adoption doesn’t outpace security and compliance.

The goal is to help organizations reimagine AI adoption with security built in, rather than bolted on after the fact.

Check Point’s Workforce AI Security is designed to give security teams control over employee AI usage without forcing a slowdown in day-to-day work.

Key capabilities include:

• Discovery of AI tools: Automatically identifies which AI tools are being used across browsers and devices, including unsanctioned or Shadow AI services.
• Usage visibility: Breaks down activity by application and user, so you can see who is using what, and for which types of tasks.
• Granular policy controls: Lets you apply policies by app and data type—for example, allowing general usage but restricting sensitive data from being shared.
• Lifecycle governance: Supports governance before launch, in production, and over time as AI usage evolves.

This approach helps organizations:

• Govern employee AI usage and Shadow AI without blanket bans.
• Enforce security and policy per interaction, rather than relying only on static rules.
• Continuously adjust controls as new tools and use cases appear.

In practice, this allows teams to keep experimenting with AI while security and compliance teams maintain the oversight they need.

Check Point focuses on securing AI where decisions are actually made—inside applications and agents—rather than only at the perimeter.

AI Agent Security (runtime protection)

• Inline inspection: Inspects prompts, outputs, and agent actions inline to detect and block prompt injection and unsafe autonomy.
• Shadow AI detection: Identifies unapproved or hidden AI usage tied to agents and tools.
• Policy enforcement without retraining: Enforces security policies on top of existing models, so you don’t need to retrain your LLMs.
• Risk-aware decisions: Understands user intent to assess risk and prevents risky connections to corporate resources.

AI Red Teaming (pre-production and continuous testing)

• Adversarial testing: Simulates real-world AI attacks and misuse to expose vulnerabilities in reasoning, workflows, and tool usage before launch.
• Prompt injection and data leakage testing: Identifies where prompt injection or data exfiltration could occur.
• Business-aware prioritization: Prioritizes risks based on business impact, not just technical severity.
• Actionable remediation: Provides concrete guidance on how to fix identified issues.
• Built for production: Designed for sub-50ms latency and is model-agnostic, working with any LLM provider.

Together, AI Agent Security and AI Red Teaming help enterprises reshape how they secure AI across the lifecycle—from design and testing through to live operations and ongoing governance.