AI Agent Security | In-Depth Guide

AI agents are almost certainly the most talked-about new technology in the world of enterprise IT. This should be no surprise, as they offer almost unprecedented potential for efficiency gains and workflow improvements.

It’s very reasonable to have concerns around this, given that AI agents may be connected to mission-critical systems and data, and are enabled to act autonomously.

Of course, each of these things has serious security implications.

So, today, we’re diving deep into AI agent security.

Specifically, we’ll be covering:

• What are AI agents?
  • Are AI agents secure?
• Security risks and threats from agentic AI
• Security benefits of agentic AI
• 10 key security measures for AI agents

By the end, you’ll have a working knowledge of whether AI agents are the right choice for your specific security needs, along with which specific measures you might want to put in place to mitigate key risks and threats.

Let’s start with the basics.

What are AI agents?

AI agents are software systems capable of autonomous reasoning and action. They are based around one or more LLMs, which have been trained on large amounts of data to interpret and generate human language.

This is then connected to various tools and other resources to enable it to act autonomously in the environment.

The goal is to replicate and augment human colleagues, such that the agent is able to receive a high-level goal, assess the relevant context, determine how to proceed, and take action to complete the task at hand.

On top of this, agents are capable of learning and improving based on previous experiences, making them a highly adaptable, effective automation solution.

As such, a huge part of their growing popularity comes down to the fact that agents enable us to automate a wide range of workflows and use cases that would be highly difficult with traditional automation tools.

So…

Are AI agents secure?

The idea of enabling AI systems to interact autonomously with our other tools and data might naturally make many IT pros more than a little dubious.

However, as with many types of solutions, the question of whether or not AI agents are inherently secure isn’t as straightforward as you may initially think.

That is, no technology is entirely free of risk.

Instead, what’s important is to understand what these risks are and how we can manage them, in order to implement a solution that’s secure for our particular situation and use case.

On the flip side, this also applies to alternative approaches that we might implement instead of AI agents.

That is, all kinds of solutions pose their own security benefits and challenges.

Therefore, it’s more effective to think about this in terms of which options, whether agentic AI or something else, are most likely to be effective in individual cases.

In the following sections, we’ll give a fuller account of these security challenges and benefits in the case of AI agents.

Security risks and threats from agentic AI

First up, we need to outline some of the risks and threats associated with agentic AI. We’ve hinted at some of the high-level issues here already, but it’s important to outline these in more detail, as some of them are relatively unique and novel.

Some of the most prominent risks and threats we’ll need to be conscious of include:

• Unauthorized access, data leaks, and exfiltration - If not properly secured, agents risk sensitive data being leaked.
• Data poisoning - maliciously manipulating the data an agent relies on to affect its outputs.
• Promp injection - malicious inputs that cause agents to perform unauthorized actions.
• Tool manipulation - unauthorized use of tools and resources the agent is connected to.
• Third-party vulnerabilities and supply chain attacks - AI agents often rely on third-party models, APIs, or data sources, which can introduce vulnerabilities.
• Oversight issues - poorly supervised agents can execute harmful commands.
• Difficulty tracing - in some cases, it may be difficult to trace the actions of AI agents, making them unaccountable.
• Hallucination - AI agents can produce fabricated or incorrect information.
• Resource overload - AI agents can consume excessive system resources, potentially leading to performance degradation or denial-of-service.

However, none of these are necessarily deal-breakers. Rather, the key is to understand the mitigation strategies that are available to us for securing AI agents.

Of course, some of these could be eliminated by simply relying on other types of solutions.

However, this would also mean sacrificing the well-documented benefits of agentic AI, both from the perspective of enhancing workflows and more security-focused advantages.

Therefore, the important thing remains to understand the use cases and scenarios where the benefits of agentic AI are likely to outweigh the potential challenges, as well as to have the right mitigations in place to maintain the security of our systems.

For a fuller discussion of this, you might like our guide to the top AI agent use cases .

Security benefits of agentic AI

As we noted earlier, AI agents also have the potential to offer important security benefits. We can think about this at two distinct levels.

Firstly, there are the characteristics of AI agents that offer advantages from a security perspective compared to human-led workflows or traditional automation tools, at least in certain circumstances.

And second, there are security-focused use cases for agentic AI that can help to reinforce our wider cybersecurity efforts.

With that in mind, some of the key security advantages of AI agents include:

• Reducing human interactions - by reducing human involvement in workflows, we can also reduce the incidence of errors and malicious actions.
• Expanding automation use cases - opening a greater range of use cases up to automation than traditional, static rules.
• Breaking down silos - fostering better collaboration, information sharing, and visibility across the organization.
• Modularity - making AI agents easier to maintain and swap out specific elements and connected tools when necessary.
• Threat detection - AI agents can be deployed as highly effective threat detection solutions.
• Identifying anomalies - establishing baselines for key security metrics and identifying anomalies
• Reducing response times - enabling us to respond to cybersecurity threats, risks, and issues more quickly.

So, in addition to offering security advantages across a wide range of use cases, AI agents are also a powerful tool in our wider cybersecurity arsenal.

However, in order to achieve this, we also need a working knowledge of the tools and techniques we can use to maintain security within our agents, helping to overcome the risks and threats we discussed earlier.

10 key security measures for AI agents

Having given a broader overview of the security implications of implementing AI agents, we can move on to checking out some of the key tools and techniques we can use to maximize security within our solutions.

This comprises a range of measures, including technical solutions as well as procedural, cultural, and organizational factors.

Specifically, the following security measures are priorities:

1. Encryption
2. Access controls
3. Authentication and authorization
4. Data classification and sensitivity labels
5. Zero trust
6. Real-time monitoring
7. Auditing
8. Multi-agent collaboration
9. Baseline agent behavior
10. Rate limiting

1. Encryption

As agents typically handle sensitive information, end-to-end encryption is vital. Data should be encrypted both in transit and at rest.

This applies to data including API tokens, user inputs, and internal context. It’s recommended to use strong encryption algorithms for secrets and credentials.

2. Access controls

Just as we would with human users, it’s imperative that we have appropriate roles in place for AI agents, limiting the resources they’re exposed to and the actions they can take. We might also implement time-limited access, with permissions expiring when a task is complete.

In addition, we’ll need to account for end-user roles, including ensuring that agents cannot take actions that are not appropriate for the specific users interacting with them.

3. Authentication and authorization

AI agents should use secure authentication standards, including client credentials for machine-to-machine communications.

Authentication and authorization standards for AI agents are also often context-aware, utilizing fine-grained authorization methods to build adaptive policies.

4. Data classification and sensitivity labels

As we noted earlier, many of the most prominent risks for AI agents surround data breaches.

Data must therefore be effectively labeled and classified, providing agents with guidance on which information is restricted and how it is intended to be used, preventing unauthorized access.

5. Zero trust

Implementing a zero-trust approach means that no system or user is implicitly trusted. This can also be applied to AI agents.

So, rather than being taken for granted, permissions and access are subject to strict verification and least-privilege principles.

6. Real-time monitoring

Many of the risks associated with AI agents can be mitigated by more effectively understanding how systems are behaving in the real world.

Real-time monitoring tools should be implemented in order to detect and flag unusual activity, helping to identify potential security breaches.

7. Auditing

Detailed audit trails can be implemented to track AI decisions and interactions. This information can then be used to provide accountability and facilitate investigations.

Depending on how we build our agents, we might be able to rely on built-in tracing and observability tools in the builder platform, or we might wish to use dedicated tools for this.

8. Multi-agent collaboration

Many agentic systems rely on multiple, task-specific agents to carry out more complex tasks.

Using multiple agents in tandem can also enhance security, including utilizing attack and defence agents within simulations of cyberattacks.

9. Baseline agent behavior

Establishing baselines of agent behavior is an essential security strategy, facilitating a range of other techniques and mitigations.

Essentially, this means determining what expected behavior is, in order to identify deviations and potential malicious activities.

10. Rate limiting

Lastly, many of the key risks associated with AI agents, including data breaches and brute force attacks, can be mitigated using rate limiting.

This means enforcing thresholds on transactions and requests, preventing large-scale exploitations from occurring.

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