What Is Agentic AI Security? Guide to Securing 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.

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 ThreatLabz found AI/ML activity increased 83% year-over-year across an ecosystem of more than 3,400 applications (ThreatLabz 2026 AI Security Report). 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$4.99 million, including an extra $1.93M if shadow AI is involved (IBM Cost of a Data Breach Report 2026), 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 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.

• Discover and govern your AI landscape (AI Asset Management): Get a 360-degree view of AI models, agents, services, and connected data assets; detect shadow AI; and assess risks like misconfigurations, exposure, entitlements, and data poisoning with guided remediation and compliance reporting.
• Control agent access and usage (AI Access Security): Apply user- and group-based policies to allow, block, or coach access to GenAI apps and AI-enabled SaaS, with prompt/response visibility and controls (including options like browser isolation and copy/paste restrictions).
• Protect every prompt and response in real time (AI Guardrails): Use inline inspection to block prompt injection and jailbreak attempts, prevent sensitive-data leakage with DLP (100+ predefined dictionaries), and moderate unsafe outputs such as toxicity, off-topic responses, and competitive or brand-risk content.
• Continuously test and improve resilience (Automated AI Red Teaming): Run scalable, automated assessments with prebuilt and custom probes (including multi-modal tests across text, voice, images, and documents), track and remediate issues in tools like Jira/ServiceNow, and map findings to frameworks such as MITRE ATLAS, NIST AI RMF, OWASP LLM Top 10, and the EU AI Act.