AI Security for Enterprises

AI, and in particular the introduction of generative AI and Large Language Models (LLMs), is transforming the business world. However, the widespread adoption of AI tools in recent years also brought major cybersecurity risks.

From copying business data into AI engines to rolling out autonomous agents with access to your most sensitive data and systems, AI usage creates a large new enterprise attack surface to secure. Safely adopting AI tools and realizing the business benefits they offer requires introducing new solutions, processes, and frameworks that prioritize AI security for enterprises.

AI Risks for Enterprise

Through advanced analytics and rapid content generation, AI tools change what is possible in enterprise operations and product offerings. Businesses can now accurately automate processes, enhance decision-making, and improve operational efficiency with AI. They can also quickly brainstorm new ideas, innovate existing products and services, develop entirely new solutions at scale, and deliver more personalized customer experiences.

But whether it is the direct use of generative AI services or indirect interactions with background tools, powered by AI, this new technology makes it easy to accidentally expose sensitive business data and systems to attackers.

Below are some of the key AI security risks and AI-enhanced threats for enterprises to consider when integrating AI tools into their operations. Each of these risks requires dedicated AI security protections to maintain the integrity of your systems.

Exposing Sensitive Business Data Through AI Use

Businesses are rushing to integrate AI into their workflows, thereby exposing sensitive business data without updating their data security practices or implementing AI-specific Data Loss Prevention (DLP) solutions. There are many ways organizations can leak sensitive internal data through AI use, including:

• Input Prompts: Sharing sensitive data with unsecured AI models via input prompts. The 2026 Cybersecurity Report found that risky prompts almost doubled (97%) in 2025, with 90% of organizations encountering risky AI prompts and 1 in 41 prompts falling into the high-risk category.
• Weak Access Controls: Unauthorized users gaining access to AI models, revealing sensitive information shared with them or the training data used to fine-tune internally developed models. This can also allow attackers to manipulate or poison model performance as well as gain access to any interconnected systems.
• Broad Default Permissions: Organizations deploying commercial AI models must ensure default permissions are not overly permissive. Broad default permissions are in place to help users quickly adopt new tools, but it often creates unintended security gaps and an increased attack surface.

Shadow AI Usage

Another AI security threat is shadow AI , in which employees use unsanctioned AI tools that bypass enterprise security programs. Unfortunately, a lot of AI use can be informal. An employee might discover a new tool they like and begin using it without informing the IT team or considering the security implications. This creates major risks and visibility gaps, stripping security teams of oversight and control, as they are unaware of how the AI is being used.

For example, an employee could use an unsanctioned AI tool to analyze customer data, inadvertently violating privacy regulations or leaking personally identifiable information (PII). Additionally, without proper vetting, unsanctioned systems can be vulnerable to various model-level attacks that compromise the integrity of enterprise data and systems.

Vulnerabilities in GenAI Applications

Generative AI applications introduce a range of vulnerabilities, exposing organizations to new attack vectors. These threats typically try to manipulate the underlying AI model into providing unauthorized access or revealing sensitive business and training data.

Examples of model-level attacks include:

• Prompt Injection: Targeting AI systems by feeding malicious prompts, forcing them to expose sensitive information.
• Model Inversion Attacks: Allow attackers to extract sensitive training data, such as personally identifiable information (PII), from AI models.
• Model Theft: Attackers steal intellectual property and proprietary information using prompts to probe the model and reveal its underlying workings.
• Adversarial Attacks: Manipulating inputs to cause harmful or incorrect outputs, reducing model performance, and affecting decision-making systems.
• Data Poisoning: Adding or changing training datasets to impact model accuracy, often introducing biases.

Beyond model-level attacks, many attack vectors exploit poor authentication or authorization processes in AI APIs to exploit enterprise models.

Excessive AI Agency

AI security for enterprises has to contend with the increasing levels of access and autonomy of AI systems, particularly agents. AI agents interact with external tools to perform specific workflows, solving problems and performing autonomous actions within the organization. Heightened access and autonomy increase the potential impact of an attack, enabling cybercriminals to perform a wider range of malicious acts.

Additionally, agents are built on new AI infrastructure that increases the enterprise attack surface. For example, agents often connect to external tools via Model Context Protocol (MCP) Servers. However, the Check Point 2026 Cybersecurity Report highlighted major risks associated with this infrastructure, analyzing 10,000 MCP servers and identifying AI security weaknesses in 40% of them.

AI-Enhanced Cyberthreats

The AI security risks discussed above focus on protecting how enterprises use the new technologies. However, there are also many AI-enhanced cyberthreats. AI acts as a force multiplier across a range of different cyber attacks, helping develop more sophisticated tactics and target more susceptible victims while also accelerating the speed and scale of campaigns through automation and other enhancements.

Examples include:

• AI-Powered Social Engineering Content: Using generative AI tools to create more convincing, personalized social engineering and deep fake content to improve the success rate of attacks. For example, with advanced Natural Language Processing (NLP) models, attackers can provide real-world examples of business communication to recreate the style and content in new phishing emails.
• Accelerated Malware Development: AI enables attackers to develop new strains of malware that evade traditional detection methods. For example, polymorphic malware that adapts its behavior and exfiltration methods, modifying its code in real time to bypass detection techniques.
• AI-Supported Ransomware: While the ransomware ecosystem has become more fragmented, the number of victims continues to increase with the support of AI technology. Smaller ransomware groups can move faster and develop more personalized extortion techniques by researching different victims. AI and automation also enable more efficient ransomware operations, leading to reduced attack timelines and negotiation periods.
• Automatic Vulnerability Scanning: Once attackers have a software flaw to exploit, they can automate the identification of vulnerabilities across different organizations to find new victims. With AI vulnerability scanning, groups can launch campaigns at scale, identify susceptible enterprise systems, and improve their success rates.

AI Governance, Compliance, and Regulatory Risks

AI governance is critical to ensuring enterprises remain compliant with an ever-growing number of regulations on data privacy, security, and ethical AI use. As AI technologies advance, regulatory bodies are introducing stricter requirements to safeguard against biases, unfair practices, and data breaches. A lack of robust AI governance can lead to compliance failures, exposing organizations to fines, legal action, and reputational damage.

Enterprises already have to navigate data privacy regulations such as the GDPR (General Data Protection Regulation) in Europe, which enforces strict controls over the collection and processing of personal data. Now, emerging AI-specific laws require transparency, accountability, and explainability in AI systems.

The US currently has no national AI regulation. However, this means organizations must deal with a variety of state-level legislation and compliance requirements. The European Union’s AI Act came into force in 2024, providing a legal framework for safe, human-centric AI use.

How Enterprises Can Combat AI Security Threats on All Fronts

AI security for enterprises requires dedicated solutions and practices to defend against these threats. This includes tools that protect everything from basic LLM use and the development of new AI agents to new AI-powered threat-prevention and automated response capabilities that can detect and mitigate the latest attack vectors.

Modern AI security for enterprise solutions should include:

• Complete AI Visibility: The ability to monitor users and identify all AI use across the organization. By eliminating shadow AI, you can track how users interact and share data with AI tools to enforce proper enterprise security controls.
• AI Data Loss Prevention (DLP): Ensure safe AI usage by securing your data. This requires identifying and classifying sensitive business data across AI workflows, even when transformed by model outputs. Therefore, AI DLP programs require semantic understanding and the ability to track sensitive data if transformed during model inference.
• Runtime Protections: Monitor how models are used and detect suspicious activity. This requires dedicated tools that can model normal, safe behavior and accurately identify anomalous activity without generating large numbers of false-positive alerts.
• Model Restrictions: By sanitizing model inputs, hard-coding constraints, and restricting how users can interact with AI tools, you can significantly reduce AI security risks. This filters potential harmful inputs to prevent prompt injection attacks and ensures AI models operate within safe boundaries.
• Red Teaming AI Systems: A proactive method of testing AI models and identifying vulnerabilities, red teaming simulates real-world attacks to see how your systems respond. By continually testing AI systems against a variety of attack vectors, you can ensure only hardened tools are in use at your organization.
• AI Agent Security: These capabilities all typically extend beyond AI model use to agents as well. However, AI agent security requires additional protections, such as toolchain isolation and ensuring that only authorized personnel have access to connected third-party systems.
• AI Security Enhancements: AI is used across threat prevention and incident response to more accurately identify attacks and minimize their impact. Most notably, AI and machine learning algorithms allow organizations to replace static, signature-based detection methods with behavioral analysis. This improves threat detection accuracy and coverage, extending protection to zero-day attacks and insider threats.
• AI Governance: Organizations require extensive governance policies that establish safe and ethical AI use across the organization, in line with relevant regulations. This includes the ability to enforce consistent policies for every AI interaction and to log interactions for compliance reporting.

Ensure Safe AI Use and Protect Against AI-Fueled Cyber Threats with Check Point

To minimize the risks detailed above, Check Point has developed an AI security platform with extensive capabilities to monitor and protect every AI interaction across your organization, from employees to applications and agents. This includes:

• Workforce AI Security: Providing complete AI visibility across browsers, SaaS, and copilots to detect risky prompts and apply DLP protections automatically
• AI Application Security: Low-latency runtime protections to block model-level attacks, including prompt injection, jailbreaking LLMs, and harmful model outputs.
• AI Agent Runtime Security: Monitor agent actions and decisions, restrict use, and prevent unsafe outcomes.
• AI Red-Teaming & Assessment: Continuously simulate attacks using real-world AI attack vectors.