Key points

• AI agents are autonomous systems that can independently perform tasks and make decisions based on user needs, streamlining workflows and improving efficiency.
• They can pose data security risks by accessing sensitive information without proper controls in place, potentially leading to inadvertent data exposure or breaches.
• Compliance and privacy regulations must be considered when deploying AI agents to ensure secure data handling and avoid legal risks.
• Metomic helps businesses secure AI agent interactions by minimising sensitive data, classifying assets, and controlling AI data access for compliance and security.

AI agents are quickly becoming a key part of how businesses operate, with many companies using them to streamline processes, automate tasks, and improve productivity. 

These autonomous systems can learn from user input and make decisions on their own, which opens up new possibilities for innovation and efficiency. However, as with any new technology, there are concerns—particularly when it comes to data security and compliance. 

With AI agents having access to large amounts of data, including sensitive information, it’s crucial to understand the risks and challenges involved. 

In this article, we’ll explore how AI agents work, the potential security issues they pose, and how businesses can stay on top of compliance to protect their data while still benefiting from these powerful tools.

What are AI agents - how do they differ from Generative AI?

AI agents are advanced systems that act autonomously, proactively responding to user needs and taking actions to achieve goals. Unlike traditional AI systems, such as generative AI, which depend on user input to generate responses, AI agents are independent. They can make decisions, adapt, and collaborate with other systems to enhance performance.

For example, while a generative AI tool like ChatGPT responds to user prompts, an AI agent anticipates needs, plans future actions, and adjusts its approach. AI agents can engage in strategic roles across customer support, project management, or process automation.

Here are a few examples of AI agents that you might have heard of, and you might even be using already:

• Virtual Assistants (Siri, Alexa, Google Assistant): These AI-driven tools assist with everyday tasks like setting reminders, sending messages, and retrieving information. They continually improve through user interactions, offering increasingly personalised support.
• Healthcare AI ( e.g, Teneo): In healthcare, AI supports clinicians by processing complex datasets and providing data-driven treatment recommendations, enhancing personalised care and efficiency.
• Smart Home Devices (Nest Thermostat): Smart home systems like the Nest Thermostat learn user preferences to optimise temperature settings, improving comfort and energy efficiency.

The rise of AI agents is evident, with 47% of businesses using AI powered digital personal assistants. These tools are improving efficiency, reducing manual tasks, and driving productivity. However, businesses must address the security and compliance risks they bring.

How do AI agents work?

AI agents are redefining how businesses operate by taking a proactive and autonomous approach to tasks. Unlike generative AI that simply responds to user prompts, AI agents can analyse data, anticipate needs, and execute multi-step plans. 

They work independently to achieve specific goals, often using external tools like SaaS platforms or APIs to extend their capabilities.

Collaboration is another key strength. AI agents frequently work together, sharing tasks and data to deliver better outcomes. For example, one agent might gather customer insights while another generates tailored responses. This approach has made AI agents particularly valuable in customer service, where 54% of companies now use conversational AI to enhance engagement and support.

From streamlining operations to personalising customer experiences, AI agents are already making a significant impact. With the global AI agents market expected to grow from USD 5.29 billion in 2024 to USD 216.8 billion by 2035, these systems are poised to play an even bigger role in shaping business innovation.

What are the data security and privacy risks associated with AI agents?

AI agents bring immense potential, but they also come with significant data security and privacy risks. Their ability to access vast amounts of organisational data can inadvertently expose sensitive information if not properly managed. This is especially true for large organisations where controlling data flows can be complex.

A key concern is unauthorised data access. AI agents often work autonomously, which means they could access or process information without adequate oversight. If access controls and policies aren’t strictly enforced, sensitive data—like customer records or proprietary business insights—could be mishandled or leaked.

Ensuring AI agents follow established data security policies is another challenge. Unlike traditional systems, these agents learn and adapt, which can sometimes lead to unexpected behaviour. Without comprehensive monitoring, businesses risk incidents like the 97% of organisations that reported security incidents related to generative AI in the past year.

Understanding these risks is the first step toward securing AI agents. The next step is exploring compliance and regulatory considerations to keep their use safe and responsible.

What are the Security Risks with AI agents?

As Generative AI (Gen AI) becomes increasingly integrated into business processes, it brings both innovative possibilities and significant security risks. Understanding these risks and knowing how to manage them is crucial for any organisation leveraging this technology.

Here, we explore the top three security risks associated with Gen AI and provide strategies to mitigate them.

1. Data Privacy and Confidentiality

Gen AI systems often require vast amounts of data to train and function effectively. This data can include sensitive and Personally Identifiable Information (PII). If not handled correctly, there’s a risk of exposing confidential data, leading to privacy breaches and regulatory penalties.

Samsung was an early and notable victim of just such a data leak incident. The tech giant was forced to ban the use of GenAI after staff, on separate occasions, shared sensitive data, including source code and meeting notes, with ChatGPT.

Management Strategies:

• Data Encryption: Ensure that all data, both at rest and in transit, is encrypted. This minimises the risk of unauthorised access.
• Anonymisation: Implement data anonymisation techniques to remove identifiable information from datasets before using them in AI training.
• Access Controls: Use robust access control mechanisms to ensure only authorised personnel can access sensitive data. Role-based access control (RBAC) is a practical approach to limit data exposure.

2. Model Security and Integrity

Gen AI models themselves can be targets for attacks. Malicious actors might attempt to corrupt the model through adversarial attacks or manipulate its outputs, leading to incorrect or harmful decisions. Adversarial attacks on AI systems can cause models to misclassify data, which is particularly dangerous in critical applications like healthcare and finance.

Management Strategies:

• Regular Audits: Conduct regular security audits of AI models to detect and mitigate vulnerabilities.
• Adversarial Training: Enhance the resilience of AI models by incorporating adversarial training, which involves exposing the model to potential attacks during the training phase.
• Integrity Monitoring: Use tools to monitor the integrity of AI models continuously. This includes checking for unusual patterns or deviations in model behaviour.

3. Ethical and Regulatory Compliance

The use of Gen AI can lead to ethical concerns and regulatory challenges, especially when AI decisions impact individuals’ lives. Issues such as bias in AI algorithms and lack of transparency can result in non-compliance with regulations like GDPR and CCPA.

Management Strategies:

• Bias Detection: Implement regular checks to identify and mitigate bias in AI models. This includes using diverse datasets and algorithmic fairness tools.
• Transparency and Explainability: Ensure that AI decisions can be explained in understandable terms. This is crucial for maintaining trust and complying with regulatory requirements.
• Compliance Frameworks: Adopt comprehensive compliance frameworks that align with relevant regulations. Regularly update these frameworks to reflect changes in the legal landscape.

🎥AI Agents Explained in 90 seconds or less

In this short video, we explore the data security risks associated with AI agents and how to mitigate them effectively.

What compliance and regulatory considerations should businesses be aware of when using AI agents?

When deploying AI agents, businesses need to keep a close eye on compliance with data protection laws like GDPR in Europe and CCPA in the US. These regulations set clear rules on how personal data is handled, and it’s up to you to make sure your AI agents stay within those boundaries.

AI agents must follow the same data protection principles as any other system. That means only collecting what’s necessary, keeping data secure, and respecting privacy rights. For example, GDPR requires businesses to be transparent about how they use data and give people the option to access, change, or delete their information.

Non-compliance can lead to significant fines—under GDPR, this could be as much as €20 million or 4% of your global turnover. Worryingly, 78% of UK companies admit they haven’t put proper safeguards in place to manage AI-related breaches.

By taking compliance seriously, you’re not just avoiding penalties; you’re building trust with your customers and showing accountability. With regulations constantly evolving, keeping your AI agents compliant isn’t optional—it’s essential.

How can businesses secure AI agent interactions?

Securing AI agent interactions is essential to keeping your data safe while harnessing the power of AI. With their ability to access and process vast amounts of information, AI agents need clear boundaries to ensure they don’t misuse sensitive data or operate beyond their intended scope.

1. Start with user access controls and encryption

A good first step is controlling who can interact with your AI agents. User access controls let you manage permissions, ensuring that only authorised individuals can access sensitive data. Encryption is another layer of protection, safeguarding data during transmission and preventing unauthorised access.

2. Use data classification and sensitivity labels

Labelling sensitive information is a simple but powerful way to guide AI agent behaviour. By classifying data, you can ensure AI systems only access what they need while keeping private or restricted data off-limits. For example, sensitive customer details could be labelled so they’re excluded from AI processes.

3. Monitor and audit regularly

Ongoing monitoring is crucial to ensure AI agents operate as intended. Regular audits can reveal potential issues, such as unauthorised data access or policy violations, and help you correct them quickly. Having a clear audit trail also provides accountability and peace of mind.

AI isn’t just a potential risk—it’s also part of the solution. In fact, 70% of organisations say AI is highly effective in detecting previously undetectable threats. With the right controls and practices in place, you can strike a balance between leveraging AI’s capabilities and protecting your data.

🔒How Metomic can help

Metomic makes it easy for businesses to manage AI agents' access to data while ensuring compliance with privacy regulations. Here's how we do it:

• Sensitive data discovery and classification: Metomic automatically identifies and labels sensitive data across your systems. This means AI agents only get access to the right data, helping you stay on top of privacy requirements.
• Streamlining access control for secure AI interactions: With Metomic, you can easily set up access controls for AI agents, ensuring they interact securely with the right data—without the need for complex administrative work.
• Ongoing monitoring and enforcement: We don’t just set things up and forget about it. Metomic keeps an eye on AI activity, automatically enforcing security policies to protect your sensitive data.
• Compliance support: Compliance is built into everything we do. Metomic ensures your AI agents stay within the lines of data protection laws like GDPR and CCPA, reducing the risk of costly fines or breaches.

Metomic makes it simple to manage AI agents securely, keep data protected, and ensure compliance—all with minimal effort on your part.

Getting started with Metomic

Getting started with Metomic is simple and designed to help you manage AI agent interactions while ensuring compliance. Here’s how to begin:

• Free risk assessment: Use Metomic’s free data security tools to assess your current data security and compliance posture. This will help you spot any gaps and identify areas where you can improve your security measures.
• Book a tailored demo: Schedule a personalised demo with our team. We’ll show you how Metomic’s features—such as automated data classification and real-time monitoring—can help you secure AI interactions and keep data access in check.
• Consult with our experts: If you’re dealing with specific compliance or data security challenges, reach out to our team. We’ll work with you to manage risks, enforce access controls, and make compliance easier for your organisation.