What is Agentic AI security?

AI agents have become a hot topic in almost every forward-thinking company today. Leaders see the potential for these systems to automate complex tasks and make real-time decisions that used to require human oversight.

But while the focus is often on what these agents can achieve, the security side of the equation is not getting the same attention. In this post, we’ll break down the key security concerns around agentic AI, and what steps you can take to build safer deployments.

Agentic AI security, aka AI agent security or AI autonomous agent security, refers to the tools and policies used to secure AI agents as they act and make decisions on behalf of humans.

A key principle is to treat AI agents as unique, non-human identities with clearly defined roles and continuous monitoring. This approach ensures that they don’t gain unchecked access to sensitive data or become an easy target for attackers.

Let’s start by looking at some of the most common security threats that AI agents face today:

• Prompt injection: Attackers create malicious prompts that override the agent’s original instructions. This can cause the agent to reveal sensitive data, or act outside of its intended scope.
• Goal manipulation: By influencing the inputs or context, criminals can shift the agent’s objectives. For example, a financial assistant may be steered to prioritize fraudulent transactions instead of legitimate ones.
• Identity spoofing: An attacker poses a trusted user or system and tries to trick the AI agent into trusting their requests. This can lead to unauthorized access or malicious actions being carried out under a false identity.
• Agent communication poisoning: When agents exchange information, poisoned messages or data can be introduced. This leads to misinformation, and wrong decisions being made.
• Resource overload: Similar to denial-of-service attacks, this attack aims to overwhelm the agent with requests or data until it becomes unresponsive.
• Backdoor insertion: Attackers tamper with input data or model parameters to create hidden triggers that cause misclassification or performance degradation at critical moments.
• Agent-targeted malware: Specialized malware designed to exploit agent environments, with the goal to hijack agent behavior, or spread through connected systems.

Next, let’s go over some of the common vulnerabilities that can make AI agent systems an easy target:

• Software flaws and vulnerable packages: Bugs in the agent’s own code or in the third-party packages it depends on can create open doors for attackers.
• Misconfigurations and over-privileged access: Weak security settings or excessive permissions can make it easier for malicious actors to exploit agents. The remedy is typically in strategic PAM (privileged access management).
• Zero-day vulnerabilities and delayed patching: Unknown flaws, combined with slow updates, give attackers a window of opportunity.
• Weak authentication and poor authorization controls: Without strong identity authentication policies, agents can be hijacked or impersonated.
• Insecure integrations and unverified external connections: When agents connect with outside services without proper validation, they may leak data or pull in malicious instructions.

Now let’s go over some security controls and prevention strategies you can use to reduce the risk of agentic security threats:

• Encryption: Secure all data at rest and in transit so that sensitive information handled by agents cannot be misused, even if it’s intercepted.
• Principle of least privilege: Grant agents only the minimum permissions they need to perform their tasks. This reduces the blast radius if they are compromised.
• Just-in-time privilege: Provide elevated access to agents only when it’s required and revoke it immediately afterward.
• Protect tier-zero assets: Keep domain controllers, identity providers, PII databases and other critical systems out of reach from agents (unless absolutely necessary).
• Monitoring framework: Develop continuous monitoring that can detect unusual behavior or signs of compromise in agent activity.
• Secure onboarding and offboarding: When agents are introduced or retired, ensure that identity setup, role assignment, key management and decommissioning are handled with the same rigor as human accounts.
• Patch management: Regularly update the agent’s software stack and underlying AI models to reduce exposure to known vulnerabilities.
• Compliance with regulations: Ensure that AI agents operate under industry regulations and internal policies, such as GDPR, HIPAA or NIST guidelines, to maintain accountability and reduce legal risk.

The stakes are higher in cloud environments because of the dynamic nature of resources, shared infrastructure and the speed at which agents can interact with services. Here are some tips to keep AI agents secure in cloud environments:

• Use strong identity and access management controls to separate agent identities from human and machine accounts.
• Segment cloud resources so that an agent compromise in one workload doesn’t spill over into others.
• Apply network security rules to restrict which services and regions agents can communicate with.
• Enable logging and auditing across all agent actions to maintain visibility and traceability.
• Use key vaults and managed secrets services instead of embedding credentials in agent workflows.
• Regularly review cloud provider security updates and apply patches to the services that agents rely on.
• Apply data residency and compliance policies to ensure agents don’t move sensitive information to unapproved regions or services.

Finally, here are some of the key trends that will likely shape the future of AI agent security:

• AI agents in cybersecurity: Today, agents are already assisting human security experts with tasks like threat detection and log analysis. In the future, their role will expand to automated remediation, and even acting as first-line defenders against cyberattacks.
• Security products for AI agents: We’ll see dedicated security tools built specifically to monitor and protect agents.
• Stronger guardrails: AI agents will increasingly include built-in safeguards and context-aware restrictions to reduce risks from common threat vectors.
• Agent-on-agent monitoring: More robust frameworks will allow AI agents to (more strictly) monitor the behavior of other agents and create a layered defense system.
• Policy-driven governance: Organizations will move toward formal frameworks where agent activity is continuously measured against compliance rules and business policies.
• Greater focus on explainability: Security solutions will emphasize transparency in agent decision-making, which will make it easier to audit and investigate when something goes wrong.