Artificial intelligence (AI) is changing the way that employees work, how companies think about technology, and how businesses process and utilize information.
However, given these systems’ need for connectivity and constant access to data, they can introduce new cybersecurity risks.
Unfortunately, while organizations have been quick to adopt AI into different aspects of their operations, their security measures may not be up to the task. An effective security strategy needs to be agile and resilient enough to mitigate future threats while still allowing AI usage to grow and expand for continued innovation and productivity.
That’s why the Zentera team wanted to share six key best practices that we believe organizations can adopt to better safeguard their enterprises against the cybersecurity risks associated with AI without interrupting its positive impact.
Six Effective Strategies to Mitigate the Cybersecurity Risks to AI
Our mitigation strategies span the different elements essential to AI security, from robust segmentation and data protection measures to comprehensive governance and incident response planning.
Segment AI Systems
1. Virtual Segmentation
Virtual segmentation improves on the concept of physical separation a step further by isolating critical AI assets, devices, and data in software.
This practice allows for more precise control over who and what can access these resources, and allows the segmentation to be dynamically changed, allowing the segmentation boundary to move and grow along with the AI system. Leading technologies like Zentera's Cyber Overlay make implementing advanced virtual segmentation capabilities straightforward and easy to manage by creating secure, isolated environments to segment and protect the nodes of AI instances. This ensures that AI systems operate within a secure and controlled environment.
Strengthen Data Security Measures
2. Access Control Policies
In addition to protecting the AI systems from a network perspective, organizations also need to set strict access policies for both AI frontends and backends.
These policies should govern who can access AI systems, under what conditions, and for what purposes. Unlike other systems that control access higher in the OSI model, Zentera's platform integrates segmentation with packet-level access controls based on user, device, and application identity, ensuring that only authorized individuals have access to sensitive AI data and functionalities.
3. Secure Storage of Training and Production Data
AI systems often rely heavily on large datasets for training and production purposes, often spread across various environments such as on-premises infrastructure, cloud services, and SaaS platforms such as data lakes. Securing this data and managing access to it becomes increasingly complex in a distributed and hybrid environment.
In turn, organizations should look to implement a comprehensive strategy, which includes:
- Using strong encryption methods for data at rest and in transit.
- Implementing robust access management tools.
- Deploying secure network overlays to connect resources safely, regardless of their location.
- Ensuring that only authorized AI systems and users have access to the data.
By protecting this data and managing access effectively, organizations can mitigate the risk of data breaches, maintain the accuracy and reliability of their AI systems, and ensure seamless integration between distributed data sources and AI applications. This approach is particularly crucial for industries handling sensitive information, such as e-commerce platforms dealing with customer purchasing data stored in cloud environments while running AI systems on-premises.
Develop Comprehensive AI Governance Policies
4. Guidelines and Protocols for AI Usage
Moving beyond the technical, organizations need to also consider the security risks related to the human element.
This is where establishing clear guidelines and protocols for AI usage within an organization is key, defining who can use AI systems, how AI can be used, what data AI can access, and how AI should interact with other internal systems.
5. Risk Assessment and Management
As AI usage grows and evolves, regular risk assessments are essential for identifying and mitigating potential new vulnerabilities in AI systems.
Therefore, organizations should ensure that their AI systems are woven into their risk assessment and management strategies so any threats are included, prioritized, and have resources allocated to mitigate them.
6. Incident Response Planning
Finally, in the event that a security incident involving AI systems occurs, organizations need to have a comprehensive plan for how they will respond. This plan should outline the steps to be taken in the event of a breach, including how to contain the threat, mitigate damage, and restore normal operations.
Organizations need to regularly update and test this plan to ensure they are prepared to respond to any cybersecurity incidents that may arise.
Develop Comprehensive AI Governance Policies
As with any new technology, mitigating cybersecurity risks to AI requires a multifaceted approach that includes segmentation, strengthened data security measures, comprehensive governance policies, and vigilant incident response planning.
Fortunately, platforms like Zentera are available to make implementing Zero Trust security principles easy and straightforward to manage, allowing organizations to constantly evaluate and authenticate access to AI and other enterprise systems. With advanced segmentation capabilities, robust access controls, and packet-level precision, platforms like Zentera give organizations the ability to be equally vigilant and adaptable—key elements to staying ahead of emerging threats and the competition.
Want to learn more about how Zentera can help secure your AI systems?
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