What Is AI Security?

AI security is important for enterprises because AI systems are now a core feature of any enterprise’s operations. Zscaler ThreatLabz found that enterprise AI/ML activity grew 83.3% YoY in 2026. Data transfer to AI tools jumped 92.6%—most of it unseen by security teams.

But many enterprises lack the visibility and controls necessary to manage the nuances of AI security. When they ran adversarial scans on enterprise AI systems, Zscaler red teaming experts found their first critical system failure within about 16 minutes. Those experts also discovered scenarios in which a single prompt triggered a high-severity issue (ThreatLabz 2026 AI Security Report). 

Without AI security tools and best practices, enterprises face major AI risks like: 

• Shadow AI and data leakage: Employees adopt new GenAI tools to improve their productivity, but many of these tools are unapproved or haven’t gone through security review. GenAI tool usage can result in compliance violations or leakage of sensitive data like proprietary code. 
• Prompt injection: Bad actors can hide malicious instructions in content that AI agents read. Those instructions can give a bad actor a foothold in an enterprise’s environment, which they can then exploit in a larger cyberattack.
• Insecure integrations and third-party APIs: Many AI tools use integrations and plugins, but those connection points are frequently insecure and therefore expand the attack surface.

AI security works in two distinct ways: it enhances traditional cybersecurity tooling, and it secures AI systems themselves.

With AI, traditional cybersecurity tools can speed up existing security processes and use pattern recognition capabilities to identify and respond to attacks quickly. AI addresses traditional cybersecurity challenges like alert fatigue, manual triage requirements, and a lack of visibility into novel cyberattack patterns.

What To Look For in AI-Enhanced Cybersecurity Solutions

As leaders evaluate cybersecurity solutions, they should look for vendors that offer the following AI capabilities:

Enterprises protect their AI systems by applying security controls at each phase of the AI lifecycle.

Step 1: Secure AI training data. Model training data is the backbone of an effective AI model. Without secure, comprehensive data, an AI model will be at best ineffective, and at worst a security concern. 

Enterprises validate training data’s security by tracking its lineage and movement before deployment.

Step 2: Protect deployed AI models. At this stage in the AI lifecycle, security teams focus on hardening AI systems from query-based attacks.

Some AI deployment security procedures include: 

• Enforcing least-privileged access controls on chatbots with API access to sensitive datasets, like customer PII. 
• Model output filtering to prevent the model from sharing sensitive information with users.

Step 3: Runtime defenses protect against prompt-related threats. Runtime for generative AI is when an AI model processes a query. Any prompt that users give generative AI models can introduce risk, and enterprises address prompt risks by: 

• Establishing guardrails that restrict the model from processing external content. External content can contain malicious hidden prompts that the model interprets as a legitimate prompt from an authenticated user.
• Enabling semantic-level prompt filtering. This type of filtering understands the intent behind queries and prevents threat actors from submitting malicious prompts.

Step 4: Continuously monitor AI models and security. Enterprises employ AI-SPM to address AI model behavior drift. 

What is Agentic AI Security?

Agentic AI security involves securing AI agents, which are AI systems that can independently execute tasks and make decisions. While GenAI takes a single prompt and produces one output, AI agents take one request and determine the steps needed to complete the task. Agents break down the user-generated goal into subtasks and then execute on those tasks without human intervention.   

AI agents typically have access to sensitive information like user credentials and internal databases. Because agents interact with other agents, external websites, and external data, they are uniquely vulnerable to compromise.

Agentic AI Risks

AI agents present a unique security risk. Because agentic AI involves chained decision-making to complete a user’s goal, it introduces risks like: 

1. Privilege escalation: AI agents are granted access to more resources than are strictly necessary to complete the assigned task. 
2. Memory poisoning: Threat actors corrupt an agent’s persistent memory with malicious instructions. A legitimate user can unintentionally execute these instructions when they engage with the agent.
3. Goal hijacking: Bad actors take over the user’s instructions and prompt the agent to exfiltrate sensitive data.

Best Practices for Agentic AI Security

• Understand your AI assets: Inventory agents, models, prompts, tools, data sources, and data flows. 
• Control who has access to what: Enforce least-privileged RBAC, strong authentication, scoped/short-lived credentials, locked-down tool permissions, and audit logging.
• Employ continuous, automated red teaming: Run recurring tests for jailbreaks, prompt injection, data leakage, and unsafe tool actions. 
• Enforce strict guardrails: Use tool allowlists, validation, sandboxing, egress limits, PII redaction, policy filters, and approvals for high-risk actions. Enforce data integrity and governance controls to protect against data poisoning attacks.

Enforcing zero trust for agentic AI is also a best practice. When paired with real-time monitoring, zero trust helps enterprises protect against common AI attack paths. 

AI security risks fall into three categories:

• Risks to enterprise AI systems like training pipelines and agents,
• Risks from AI-orchestrated cyberattacks, and
• Regulatory risks. 

1) Risks to Enterprise AI Systems

Enterprise AI systems access sensitive business information and processes, which makes them a high-value target for bad actors. Enterprise AI system risks include the following: 

• Prompt injection: Bad actors manipulate AI inputs and agents to exfiltrate data.
• Training data poisoning: Enterprises fine-tune AI models with internal data like customer contracts, call center transcripts, and HR records. Threat actors can poison these models with malicious data or create backdoors into AI systems.
• AI supply chain attacks: Threat actors seed malware and backdoors in the public repositories that enterprises use for their pre-trained AI models.

2) Risks From AI-Orchestrated Cyberattacks

AI acts as an attack amplifier. ThreatLabz research indicates that threat actors use AI to speed up their existing TTPs.

Here are the top ways that bad actors are using AI in their operations: 

• AI-enhanced phishing and social engineering: Threat actors use AI site builders to create high-fidelity phishing sites, apps, and lures. ThreatLabz reviewed over 400k AI-generated site instances and found that 9% of them were probably used for social engineering attacks.
• Faster vulnerability exploitation: Bad actors use AI scanning tools to quickly find and exploit unpatched vulnerabilities.
• Shadow AI: Enterprises don’t always know which GenAI tools their employees are using. Security teams can’t enforce controls on apps they can’t see.

3) Regulatory and Compliance Risks

Compliant and ethical enterprise AI usage requires that organizations enforce consistent AI security guardrails. Without such guardrails, they are vulnerable to risks like: 

• Data privacy violations: Regulatory frameworks like GDPR and the California Consumer Privacy Act outline how AI systems must manage sensitive data. Enterprises without AI data governance enforcement can violate these compliance frameworks and pay hefty fines.
• Lack of transparency: Because many AI systems make decisions without transparently outlining their decision criteria and processes, it’s easy for these systems to make biased decisions and erode trust.
• Intellectual property violations: Enterprises that unknowingly use copyrighted data to train their AI models are vulnerable to IP infringement claims.

Enterprises can manage AI regulatory and compliance risks using frameworks like NIST AI RMF, which helps security teams map, measure, and manage risk across the AI lifecycle.

AI Security vs. Traditional Cybersecurity vs. Cloud Security

AI security, traditional cybersecurity, and cybersecurity share a common goal of preventing unauthorized access, but these technologies differ in scope. AI security protects models, prompts, and training data from threats like prompt injection. Traditional cybersecurity and cloud security focus more on protecting networks, identities, and configurations.

Enterprise AI security measures are practical enough for daily use and strict enough to meaningfully reduce AI security risks.

• Create a list of every GenAI tool and AI-enhanced SaaS app that uses AI in your enterprise environment. To prevent shadow AI creep, continuously update this list.
• Prevent your SaaS apps from automatically using AI-powered features. This way, your security team can align those features with your company’s security posture.
• Enable least-privileged access for every user and system that can engage with an AI model.
• Enforce inline inspection across all of your AI and ML traffic.
• Evaluate every AI model’s lineage and supply chain dependencies before adopting it.
• Proactively find weaknesses with adversarial testing and red teaming exercises.
• Update AI governance frequently to keep it up-to-date with the latest emerging risks and compliance trends.

Zscaler extends zero trust principles to AI security. With Zscaler, organizations can find shadow AI and govern AI access. 

The platform protects sensitive data where it’s most likely to leak: in prompts, responses, and AI-connected workflows. 

Zscaler also helps operationalize AI governance and compliance. The platform maps risks to evolving frameworks and policies so that teams can prioritize remediation and stay audit-ready.

To learn more about how Zscaler can secure your AI, read the product brief.