How to Secure AI Agents: Models & Risks Explained

An AI agent is an algorithm or model that performs tasks semi-autonomously based on user inputs. This includes generative AI (GenAI) that can create text or images, such as ChatGPT, as well as systems like the recommendation features in streaming apps. AI agents "learn" from vast amounts of data to provide robust outputs, making them extremely powerful, but also highly vulnerable.

How AI Agents Work—and Introduce Risk

AI agents process inputs such as text, images, or raw data, and then produce outputs based on predefined objectives. To do this, they rely on training datasets, machine learning, and massive amounts of computing power. However, this way of operating introduces certain risks:

• Data leakage: Users may input large amounts of sensitive data, such as customer records, medical histories, and company secrets, into AI agents. Because the agents learn from inputs, they can expose this data in outputs if not properly configured.
• Privacy and compliance failures: If they process personal data, AI agents can complicate efforts to comply with regulations like GDPR and CPRA. For instance, failing to anonymize inputs or improperly sharing data across borders can violate privacy or governance rules.
• Implicit bias and unintended outcomes: Biases in AI agents' training datasets can cause them to produce discriminatory or inaccurate outputs. This can have a wide variety of negative business outcomes, such as poor recommendations and loss of efficiency.
• Insufficient monitoring and governance: AI agents not subject to regular monitoring and review can experience "model drift" over time. This can harm their output quality and, critically, their security posture, possibly leading to undetected flaws or compliance issues.

These terms are often used interchangeably, but they refer to different layers of the AI stack. Understanding how they differ helps you apply the right security controls—whether you’re securing a base model, a conversational interface, or an autonomous system that can take actions.

Threat actors constantly find new ways to exploit, corrupt, and subvert AI models, proving that basic guardrails aren't always sufficient protection. These are some of the most common AI agent threats and risks:

Prompt injection attacks use manipulative inputs to change an AI model's outputs or behavior, or bypass its guardrails. For instance, an AI agent could be prompted to roleplay as a user without safeguards against divulging private data.

Data poisoning attacks corrupt an AI model by contaminating its training dataset. For example, an attacker could insert false financial data into training data to change how a model makes risk predictions.

In a model inversion attack, threat actors repeatedly query an AI model and infer sensitive data by reconstructing it from the outputs. For example, an attacker could query an AI trained on healthcare data to reveal specific conditions or treatments linked to individuals.

Adversarial manipulation attacks use inputs designed to confuse an AI model and force incorrect outputs. For example, a threat actor might feed fraudulent images to a model that detects manufacturing defects, causing faulty products to pass quality control.

AI supply chain attacks exploit AI models that rely on third-party APIs, libraries, or secondary models to gain unauthorized access or execute prompt attacks. For instance, an attacker could use a corrupted library update to inject malicious code or alter a dependent AI system's outputs.

Shadow AI risks stem from the use of AI models that an organization has not approved. Unsanctioned AI apps are not inherently malicious, but because the organization has no oversight, they can be an easy avenue for data leaks.

Regardless of the risks, AI adoption continues to skyrocket, with AI tool usage up a massive 3,464.6% from 2023 to 2024. Along those same lines, Gartner predicted that 80% of organizations would deploy AI models by 2026. If these trends hold true, managing and securing AI data will become both more critical and more challenging. However, most organizations lack the control, visibility, and tools to manage and secure AI effectively. This leaves them with three options: embrace AI despite the risks, restrict its use entirely, or invest in tools and strategies that enable safe adoption.

The first of these options is an unsustainable approach for any organization dealing with large amounts of sensitive data. Gartner projects that improper use of AI will lead to 40% of breaches by 2027. With an average cost per breach of US$4.44 million, including an extra $670,000 if shadow AI is involved (IBM, 2025), it's impossible to justify the risk of letting AI go unchecked. The second option quickly reduces AI-related cyber risk, but at the expense of user satisfaction and productivity. AI tools save workers an average of 52 minutes per day, giving them back time for other work, professional development, and more.

For most enterprises and government agencies exploring the benefits of AI, that leaves the third option. Investing in effective AI security unlocks AI-driven speed and innovation without the cyber risk, and without incurring a cost in productivity and user experience.

Benefits of Effective AI Agent Security

The right solutions can help improve decision-making, simplify compliance, and protect against cyberthreats, enabling organizations to:

• Safely use public AI tools: Protect sensitive data while reducing shadow AI risks and ensuring safe access to popular AI apps.
• Secure private AI systems: Prevent attacks like prompt injections and data poisoning while keeping AI models and training data safe.
• Block AI-powered threats: Stop advanced cyberattacks by securing data, shrinking the attack surface, and blocking malicious actions.
• Boost productivity with confidence: Use AI to drive efficiency and innovation without risking data exposure or misuse.
• Gain better insights and control: Monitor all AI usage, block shadow AI, and log prompts and responses for greater oversight.

In the race to capitalize on the benefits of AI, it can be tempting to prioritize speed and innovation over security. However, effective security for AI agents requires a proactive strategy based on the tenets of zero trust. Here are five practical steps to take, rooted in cybersecurity best practices:

1. Block shadow AI and ML domains at first: Initially, block access to unauthorized AI tools and domains in your organization completely. This lets your teams take time to understand possible risks and reduce the potential for data leakage.
2. Approve individual AI tools based on security standards: Carefully evaluate the security, privacy, and compliance profiles of AI applications your departments and users want to adopt. This includes widely used tools like Microsoft Copilot or ChatGPT.
3. Host AI tools in secure private servers: Deploy AI agents on your organization's private infrastructure. This ensures full control over the data, models, inputs, and resources underpinning the agent, minimizing exposure.
4. Control access using zero trust tools: Implement a zero trust architecture, including single sign-on (SSO), multifactor authentication (MFA), TLS/SSL inspection, and microsegmentation, to ensure least-privileged access to your AI agents, data, and workflows.
5. Enforce data loss prevention (DLP) policies: Apply DLP to your AI models and workflows to control how data enters, traverses, and exits your environment. Complete visibility into AI data interactions provides essential context to help prevent breaches.

Zscaler AI Security provides visibility, control, and protection to ensure safe adoption and use of AI agents. It secures AI tools with:

• Real-time data mapping: Track sensitive data flows inside and outside your organization and spot risky interactions instantly.
• Shadow AI detection: Identify and block unauthorized AI tools before they compromise sensitive data.
• Risk-aware isolation: Contain high-risk AI activities within an isolated browser using dynamic, risk-based policies.
• Behavior monitoring: Continuously monitor user actions to flag risky behaviors and highlight training opportunities.
• Customizable access controls: Set AI-specific usage policies to allow approved tools while blocking risky or unapproved ones.
• Red team testing: Test for AI Vulnerabilities in models and services.

Built into our unified Data Security platform, AI Security helps you:

• Minimize AI data risks: Ensure safe AI usage without exposing sensitive data.
• Control data uploads: Apply granular controls to allow prompts while preventing bulk data uploads.
• Track AI usage trends: Gain prompt-level insights into how employees are using AI tools.
• Educate users: Use Zscaler Workflow Automation to train users on AI risks and reinforce best practices.