AI in Cybersecurity: Navigating GDPR, Privacy Laws, and Risk Management

Introduction

Artificial intelligence (AI) in cybersecurity has unmatched potential when it comes to achieving superior solution efficacy, automation, and more. However, with great power comes great responsibility—when leveraging AI, organizations must grapple with both the compliance implications and the ethical dilemmas involved in doing so. In particular, navigating privacy regulations like the General Data Protection Regulation (GDPR) while harnessing AI-powered cybersecurity tools is simultaneously highly complex and incredibly important. As a result,  organizations are searching for how they can leverage AI in cybersecurity without compromising compliance.

The intersection of AI and cybersecurity

Artificial intelligence and machine learning algorithms have the potential to transform cybersecurity for any organization. That’s because AI can enhance virtually any aspect of a company’s security efforts, including threat protection and data protection. 

In terms of cyberthreats, one clear advantage of tools leveraging AI is their capacity to deliver real-time, fine-grained threat detection. Unlike human analysts, AI can process wide varieties of data concurrently. This capability enables user and entity behavioral analytics (UEBA) to identify anomalous user behaviors, and equips AI-powered cloud sandboxing to proactively identify files that are likely malicious. As a result, they are able to pinpoint potential risks before attackers can gain a foothold. 

Other AI innovations in threat protection include automated threat hunting as well as phishing defenses that use natural language processing (NLP) to parse malicious emails and flag subtle linguistic anomalies. While all of this minimizes cyber risk, it also reduces the prevalence of false positives, empowering cybersecurity teams to focus their energy on legitimate threats.

When it comes to keeping sensitive information safe, leading solutions can employ AI/ML to protect data more effectively and efficiently. As an example, AI-powered data discovery can scan an organization’s IT ecosystem (devices, clouds, apps, user sessions, etc.), automatically classify all of the data that it finds, and generate reports for administrators about what data is where. This is done without admins needing to configure any DLP dictionaries, engines, or policies. In other words, AI enables organizations to identify sensitive data everywhere, while simultaneously minimizing admin time requirements and reducing management overhead. 

Clearly, AI's impact on cybersecurity is tangible and transformative.

Understanding GDPR and privacy law challenges

Despite its countless benefits, deploying AI within cybersecurity introduces friction with data privacy laws, particularly Europe's GDPR. GDPR's core aim is to protect personal information, and it does so by regulating how organizations handle data. But AI and ML, by their very nature, use vast amounts of training data and are typically trained, at least in part, with sensitive or personally identifiable information (PII). This can enable (and even constitute) a data breach, which leads to regulatory penalties, reputational damage, and customer churn.

On top of that, compliance hurdles arise due to the collection of the data that is needed to effectively train AI models (not to mention the data that inline security solutions collect merely as a part of their regular functioning). That’s because GDPR also requires organizations to get explicit consent from individuals in order to collect their data for any purpose. In addition to all of this, there are rigid conditions for data protection, storage, and deletion. 

Finally, the "black box" dilemma inherent to many AI models complicates compliance further. GDPR emphasizes transparency and accountability, but many advanced AI algorithms are opaque, making it challenging for organizations to explain precisely how personal data is processed and how decisions are reached. Without clear interpretability, demonstrating compliance becomes an arduous legal and technical task.

All of this is why organizations must carefully balance the benefits of robust AI-driven protections with stringent GDPR compliance requirements—otherwise, there can be significant repercussions.

How to navigate GDPR, privacy laws, and risk management with AI

Successfully incorporating AI into cybersecurity while maintaining compliance requires a strategic approach anchored in privacy-first principles, transparency, purpose-built security tools, and organizational collaboration.

Privacy-first AI solutions

Because AI-based security solutions process sensitive data, organizations must prioritize anonymization, pseudonymization, and encryption capabilities when selecting their tools of choice. By obscuring personal identifiers, cybersecurity teams can protect data privacy while still leveraging AI's analytical power. This approach significantly reduces the risks associated with data breaches and ensures adherence to GDPR's stringent data protection principles.

Explainable AI systems

Transparency is critical under GDPR, making explainable AI (XAI) crucial. Organizations should implement AI-based solutions whose decisions and data processing methods can be clearly articulated and demonstrated. Explainability not only fosters regulatory compliance but also builds trust among customers and stakeholders who expect accountability in how their personal data is handled.

AI solutions prebuilt for compliance

When evaluating AI cybersecurity tools, organizations must select  solutions that are designed themselves to comply with privacy regulations like GDPR and CCPA. This is because data controllers (the organizations) are required to work with data processors (the security vendors) and implement services (the AI-powered security solutions) that adhere to the standards of GDPR. If a data processor or its services violate GDPR, it can lead to consequences for the data controller. 

At the same time, organizations should deploy security offerings complete with out-of-the-box functionality that can help them to ensure their own GDPR compliance. For example, they should leverage data loss prevention (DLP) solutions with prebuilt dictionaries that let them identify GDPR-regulated information across their IT ecosystem. These types of capabilities streamline the organization’s ability to control said data, while reducing the potential for human error that can arise during manual configuration. As an additional benefit, this embedding of compliance considerations into the operational core of cybersecurity also helps to simplify risk management over both the short and the long term.

Collaboration between departments

Navigating privacy laws and the risks associated with AI demands cross-functional collaboration. Integrating perspectives from cybersecurity, networking, legal, and compliance teams ensures thorough assessments of potential risks and regulatory complications, as well as comprehensive solutions. Such collaboration enhances an organization's ability to foresee and mitigate problems proactively, rather than reacting after compliance issues or cyber breaches arise.

Future outlook: AI and evolving privacy regulations

As technology advances, privacy regulations like GDPR will undoubtedly continue to evolve and influence how organizations protect data. Likewise, AI’s role in cybersecurity will only expand, and while that will create new opportunities, it will also lead to new complexities. As such, businesses must stay vigilant and adapt as needed if they want to keep pace with advancing laws, technologies, and cyberthreats. Ultimately, doing so will require ongoing investment in education, processes, proactive regulatory oversight, and, critically, leading cybersecurity.

In this shifting regulatory landscape, adopting security frameworks built on zero trust becomes increasingly necessary. A zero trust architecture functions based on the principle of least-privileged access. This principle requires that only authorized entities are given access to IT resources and data, and that they are given only the minimum amount of access necessary to do their jobs—at the moment they need it. 

A platform providing this architecture proxies traffic and serves as an intelligent switchboard that delivers secure any-to-any connectivity, in a one-to-one fashion, without extending the network to anyone or anything (which eliminates the possibility of lateral movement on the network). It governs access based on myriad contextual factors so that it can ensure nothing is exposed to unnecessary risk. 

In other words, zero trust inspects all traffic, minimizes excessive permissions, controls access to IT resources, monitors the flow of sensitive information, enforces granular data security policies in real time, and provides a wealth of other functionality that aligns seamlessly with GDPR's fundamental principles. As a result, organizations can confidently protect data everywhere, leverage AI-based tools (both for security and otherwise), and maintain compliance with GDPR. 

Zscaler: Security AI and compliance

If you’re curious how you can find a zero trust offering that will check all the boxes mentioned throughout this blog post, look no further. The Zero Trust Exchange platform delivers everything you need when it comes to security, AI, and regulatory compliance. With Zscaler, organizations can: 

• Deploy airtight architecture provided by the original pioneer and continued innovator in zero trust, enabling systematic cyber risk reduction
• Partner with a trustworthy data processor whose platform is built to comply with global standards like GDPR, ISO 27001, FedRAMP, SOC 2, and many more
• Control the flow of data to any destination (including AI systems built to ingest massive volumes of information), and prevent data breaches
• Accelerate innovation by gaining visibility into the employee usage of AI tools, like generative AI apps, and enforcing granular policies that control access
• Leverage AI-powered capabilities that can further enhance security posture while simultaneously improving automation and productivity
• Stop sophisticated AI-based cyberattacks through a complete suite of protections that identify and block threats at every step of the attack chain
• Maintain full compliance with global regulations like GDPR, minimizing the risk of penalties, reputational damages, and other consequences

To learn more about zero trust, join part one of our webinar series, Zero Trust, from Theory to Practice, which is designed to guide you throughout your entire zero trust journey.

To learn more about the ways that we combine zero trust and AI to solve modern IT problems, download our white paper, Zero Trust + AI: Secure and Optimize Your Organization.