Check Point AI Security for Safe Enterprise AI Adoption

Safety First! – Enabling Safe AI Adoption

AI is the new buzzword. You’d be hard-pressed to go a single day without hearing someone talk about it or seeing an ad discussing how AI is changing the way we do almost everything. And just like every other shiny new thing before it, AI brings quite a bit of both excitement and concern. It’s exciting because now organizations can do more faster and look good while doing it.

On the other side of the coin, though, AI brings a lot of inherent risk to the table. Most of us have heard stories of people manipulating AI chatbots to get high-value items for insane discounts, runaway AI agents deleting company data and employees unknowingly sharing confidential and sensitive information to third-party large language models (LLMs). These types of events even lead to some organizations choosing to block the use of AI tools company-wide. The reality is that at some point, most, if not all, companies will be forced to jump on the AI bandwagon to remain competitive in their respective industries. Let us take a few minutes to understand how organizations can enable safe and secure use of AI.

Not All “AI Security” Is Actually AI Security

Before we dive into the how and what of AI security, we need to examine what organizations actually need – and what they don’t. Many vendors are approaching CISOs with the prospect of AI Security, when in reality, all they are offering is limited visibility or carte blanche prevention of AI utilization. True native AI Security goes much deeper than blocking applications or reporting on LLM usage.

Many vendors are also bringing long-term deployment projects to the table for AI Security. CISOs are instead looking for something they can roll out in the same quarter. AI adoption is moving much faster than most organizations can handle, and they need something they can implement quickly to avoid becoming the next victim of sensitive data loss, rogue agents or other common AI risks.

Next, we will dive into some of these AI risk surfaces and discuss solutions to help eliminate them.

The Human Factor: Secure GenAI Usage

Generative AI (GenAI) tools offer a multitude of ways to help increase employee productivity, such as crafting professional emails, summarizing large datasets and even building training schedules. Because these tools are widely used and freely available, discovering and governing their use has become nearly impossible for most organizations. GenAI tool usage also creates data exposure risks and compliance challenges.

In order to understand how to protect employee usage of GenAI tools, CISOs should seek answers to several questions:

1. What apps, either sanctioned or unsanctioned, are users frequently using?
2. How do we go about governing this usage?
3. What types of data are employees sharing with these tools?
4. Are the apps themselves secure and compliant?
5. What are the users using these apps for?
6. How can we secure autonomous agents and MCP actions?

Discover: The first step to protecting GenAI use is discovery. The discovery phase must provide full coverage of everywhere and every way employees interact with AI, including web apps, SaaS integrations, browser extensions, desktop agents and developer tools. 

Assess: Once we have the full picture of who is using which apps and how they are using them, we can begin to assess the AI risks that are relevant to our particular security and compliance requirements.

Govern: Now that we understand the contextual risks and compliance requirements, we can create flexible, granular policies to prevent the use of risky applications and control how employees interact with sanctioned apps, e.g., blocking the sharing of certain types of sensitive information.

Protect: The final step is to enforce these policies by blocking unsafe interactions in real time with AI-powered guardrails and data loss prevention (DLP).

Image

Fig 1. Check Point’s Workforce AI Security protects users from unsafe GenAI usage

Check Point’s Workforce AI Security solution provides complete protection for employees across the whole organization, regardless of how they are interacting with LLMs and other GenAI tools. Different policies can be applied to managed vs. unmanaged apps, and they can be set by the app, per user and per data type. The solution is easy to deploy and can be implemented within minutes across browsers and devices. For more information or to request a demo, click here.

Agentic AI: Bots Will Be Bots

AI-powered agents have changed the game, adding many autonomous capabilities to AI toolsets. This introduces so many possibilities, such as data retrieval, record modification and even workflow and API triggers. This autonomy simplifies redundant tasks and complex workflows, freeing workers to focus on more qualitative tasks.

However, with great power comes great responsibility, and AI agents definitely have the power. Autonomy means access to internal databases, personally identifiable information (PII) and workflow details. Many of these systems operate without clearly defined boundaries, so we need to shift our security strategy to align with them. Figure 2 below shows how traditional controls fall short.

Image

Fig 2. Traditional controls fall short with AI – Reference

To solve for these gaps in traditional protections, we need a targeted solution that can address the following with regard to agentic AI risks:

• Prompt attacks – a valid solution should be able to detect prompt injections, jailbreaks or user manipulation to prevent LLM system prompts
• Data loss – prevent sensitive information and PII from being unintentionally exposed in prompts or LLMs
• Harmful content – detect hate speech, sexual, violent or vulgar content in user prompts or LLM outputs
• Malicious links – detect phishing or malicious links from domains that are not allowed
• Custom threats

The best approach is real-time monitoring, runtime guardrails and response capabilities.

Runtime Protection: It is important to be able to block threats as they occur without sacrificing accuracy or efficiency. To stay up to date on the latest attacks, a robust intelligence feed is necessary. Flag malicious actors early, so they don’t become a problem later. 

Monitoring and Visibility: Identify AI tool utilization and interactions, detect prompt injection and other attack types and data leakage and content violations. Stay compliant by logging blocked threats and bad user/app behavior.

Centralized Control: Manage all policies from a single platform. Provide customizable policies out of the box. Keep policies consistent across applications without implementing any code changes. Maintain a good user experience with low latency and minimal false positives.

Check Point’s Lakera Guard solution takes this approach to protecting interactions between apps and users, as well as agent interactions with other apps/LLMs and third-party AI tooling. It is available as a cloud-hosted solution or on-premises. It is powered by an adaptive engine that is continually improved by an up-to-date threat intelligence platform that pulls from millions of real-world attacks. For more on how this solution works and is deployed, click here.

AI Red Teaming: Knowledge Is Power

Before deploying AI applications, an organization should understand and, if necessary, remediate any vulnerabilities through pre-deployment assessments and red teaming.

AI Security Testing: Experts help uncover critical vulnerabilities that automated tools miss.

Full Coverage: Assess risks associated with data leakage, prompt attacks and multi-agent systems.

Intelligence: Understand your environment better through detailed findings and remediation recommendations.

Check Point’s Lakera Red solution provides pre-deployment assessments and red teaming, either as an expert-led session or as automated risk evaluations and testing. Continuous red teaming engagements lead to better and more extensive AI-based threat research that also helps improve Lakera Guard’s threat prevention capabilities. For more on Lakera Red, see here.

The AI Defense Plane

In order to address AI risks across employees, agents and applications, we need a holistic approach that provides consistency across layers. Proper AI defense should allow an organization to discover, protect and govern all AI interactions within the organization. Policies, logs and telemetry should be consistent across all edges of the plane, providing unified control and visibility organization-wide. Figure 3 details a complete, unified architecture for AI defense.

Image

Fig. 3 Check Point AI Defense Plane – Reference

Conclusion

AI adoption is becoming a necessity for companies that wish to remain competitive. It does introduce risks to data, systems and workflows that can get out of control quickly. However, safe adoption is possible with careful planning and an insistence on providing complete and comprehensive controls that go beyond simple guardrails. Incorporating a unified defense architecture can greatly reduce the risks across all AI attack surfaces without impacting the user experience or introducing latency to critical business processes. Check Point can help guide this process with solutions, expertise and intelligence. To find out more, check out the white paper, Agentic AI Security: The Enterprise Playbook.