What Is Agentic AI Security? | Zscaler
What Is Agentic AI Security? Guide to Securing AI Agents
Agentic AI security is the practice of protecting AI systems that can plan, decide, and act on their own, i.e., systems with access to sensitive data, connected tools, and the ability to carry out multi-step tasks with little human oversight. Where a chatbot responds, an agent acts, and that distinction is where the risk lives. Zscaler gives organizations the visibility and controls to close that gap without slowing down what AI makes possible.
Overview
What Is an AI Agent?
An AI agent is a goal-driven system that uses AI models, data, and connected tools to complete tasks semi-autonomously.
In simple terms, AI agents can:
- Interpret user requests
- Make decisions based on context
- Interact with apps, APIs, or data sources
- Carry out multi-step tasks with minimal supervision
Why security matters: Because AI agents can access sensitive data and take actions on behalf of users, they require strong controls to prevent misuse, data leakage, and compliance violations.
What Are AI Agents?
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.
AI Agents vs. Chatbots vs. LLMs
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.
| Category | AI Agent | Chatbot | LLM (Large Language Model) |
|---|---|---|---|
| What it is | A goal-driven system that uses an LLM (and tools) to plan, decide, and execute tasks semi-autonomously. | An application/interface that uses an LLM (or other NLP models) to conduct a conversation with a user. | A foundational model that generates or transforms text (and sometimes images/code) based on patterns learned from training data. |
| Primary purpose | Complete multi-step tasks and workflows (research, file ops, ticketing, automation) with minimal supervision. | Provide Q&A, support, and guided interactions through dialogue. | Produce language outputs (answer, summarize, classify, draft, extract). |
| Typical inputs/outputs | Input: Goals + context + tool results; Output: Actions taken + final response (often with logs/state). | Input: User messages; Output: Conversational responses (may include links/forms). | Input: Prompts/context; Output: Generated text (or structured text like JSON). |
| Autonomy & tool use | High autonomy; commonly orchestrates multiple tools/APIs, uses memory/state, and iterates until a goal is met. | Low–moderate autonomy; may call limited tools (e.g., knowledge base lookup). | Low autonomy by itself; tool use only when integrated by an app/framework. |
| Key security focus | Strongest controls: Least privilege for tools, action authorization, secret management, DLP for inputs/outputs, monitoring, and containment for high-risk tasks. | Conversation data protection, authentication, safe retrieval (RAG), logging, and user/prompt governance. | Model/tenant isolation, training data protection, prompt/response controls, abuse prevention, and output filtering. |
Top Cybersecurity Threats AI Agents Face Today
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.
Securing AI Agents: Challenges & Considerations
Regardless of the risks, AI adoption continues to skyrocket, with ThreatLabz found AI/ML activity increased 83% year-over-year across an ecosystem of more than 3,400 applications. 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.
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.
Best Practices for AI Agent Security
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:
- 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.
- 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.
- 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.
- 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.
- 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.
Proactively Secure AI Agents with Zscaler
Zscaler AI Security helps organizations adopt AI agents safely by combining discovery and governance with inline controls that protect every AI interaction. With visibility into sanctioned and shadow AI, policy-based access controls, real-time guardrails, and continuous automated testing, teams can reduce data loss and security risks without slowing down AI initiatives.
Embrace AI agents with comprehensive security throughout the entire AI lifecycle.
FAQ
What Is Agentic AI Security?
Agentic AI security focuses on securing AI agents that act autonomously, ensuring their processes comply with appropriate governance and regulatory frameworks.
What Types of Attacks Target AI Agents Today?
Common attacks include prompt injection, adversarial manipulation, data exfiltration, and supply chain compromises, all exploiting vulnerabilities in AI workflows and data handling.
How Can Organizations Apply Zero Trust to AI Models?
Zero trust enforces strict access controls, user authentication, and traffic inspection to protect AI agents and their associated datasets.
What Is AI Red Teaming, and Why Does It Matter?
AI red teaming uses simulated attack scenarios to identify and remediate vulnerabilities in AI agents, helping organizations stay ahead of evolving threats.
Which Frameworks Govern AI Security and Compliance?
Frameworks like GDPR, CPRA, and NIST AI RMF establish clear protocols for securing AI data, models, and decisions.
How Can CISOs Assess Vendor AI Security Posture?
CISOs should evaluate tools for comprehensive AI visibility, compliance alignment, and integration with existing security platforms.