The Evolving Role of AI in Data Protection

Each year, Data Protection Day marks an opportunity to assess the state of privacy and security in the midst of technological innovation. This year’s inflection point follows a robust dialogue on AI from last week’s World Economic Forum Annual Meeting in Davos. As CrowdStrike participated in these discussions, we emphasized the importance of leveraging AI to defend against ever-evolving cyber threats and protect the very data and workloads used to power AI.

The Evolving Role of AI in Data Protection

Embracing Privacy-by-Design and Secure-by-Design is paramount in everything from the training data that powers AI models to the queries used to automate productivity. While AI is often framed as posing a risk to privacy, it’s important to recognize that AI is critical for protecting data against cyber threats, thereby becoming critical for modern privacy. AI-powered systems can detect and respond to threats faster and more accurately than traditional methods, making them essential in our defense against sophisticated cyberattacks and data breaches.

CrowdStrike, for instance, has pioneered the use of AI in cybersecurity to identify adversary behavior and combat sophisticated attacks. Our AI-driven approach demonstrates that effective security and privacy protection go hand in hand. For example, we incorporate Privacy-by-Design principles into how we train data, and the Falcon Platform is designed to protect customers against data breaches that threaten privacy. This approach is particularly crucial as we face the rise of dark AI, where cyber threat actors use AI to conduct faster, more sophisticated attacks that often go undetected. This is why we now offer AI Red Team Services to help organizations assess the security of their own AI implementations.

The Power of AI in Cybersecurity

CrowdStrike’s approach to AI in cybersecurity is multifaceted and continuously evolving:

1. Threat Detection: AI excels at pattern recognition and anomaly detection, allowing it to uncover subtle signs of cyber threats across vast datasets. Our AI-powered indicators of attack (IOAs) can identify potential threats before they materialize into full-blown attacks.
2. Response and Mitigation: AI-mediated threat response leads to swift and effective action, significantly reducing response time. This is crucial in an era where attacks can spread across networks in seconds.
3. Vulnerability Management: AI-native tools provide continuous monitoring and automated scanning for security weaknesses. They can prioritize vulnerabilities based on real-world threat intelligence, ensuring resources are focused on the most critical issues.
4. AI-Assisted Threat Hunting: AI enhances the work of human analysts, combining human intuition with AI’s data processing capabilities. This synergy allows for more effective and proactive threat hunting.
5. Streamlined Analyst Experience: Generative AI, like CrowdStrike Charlotte AI™, is transforming the security analyst experienceby allowing natural language queries and simplifying complex data analysis. This democratizes cybersecurity, allowing users of all skill levels to leverage advanced security capabilities.

Protecting AI Itself

While AI is instrumental in protecting data, we must also consider the protection of AI systems themselves. This is a critical aspect often overlooked in discussions about AI and data protection. CrowdStrike’s approach to AI security involves:

1. Data Operations: Ensuring the integrity of AI models through carefully curated training data. This includes rigorous processes for protecting our corpus against adversarial ML attacks.
2. Continuous Improvement: Constant refinement of models to adapt to new threats. Our adversarial pipeline, for instance, allows us to generate new adversarial samples to train our machine learning models, increasing their effectiveness against evolving threats.
3. Privacy-by-Design: Developing AI systems with Privacy-by-Design principles in mind. This helps to leverage AI in a manner designed to respect user privacy while delivering robust security.
4. Transparency and Accountability: Clear documentation of AI systems’ capabilities and limitations. This transparency is crucial for building trust with our users and complying with emerging AI regulations.

The Human Element in AI-Driven Cybersecurity

Contrary to some narratives, human expertise remains crucial in AI-driven cybersecurity. At CrowdStrike, we have embraced human-machine collaboration to deal with the speed, volume, and advancing sophistication of adversaries. Our approach involves:

1. Ground Truth Generation: Human experts, like CrowdStrike Falcon® Adversary OverWatch™ threat hunters, provide crucial ground truth data for training and evaluating AI systems. This human-validated content is a key differentiator in the effectiveness of our AI models.
2. Active Learning: AI flags incidents where human review can provide the most value, ensuring human attention is spent where it matters most. This approach, known as the “fast loop” and “long loop,” allows our AI to continuously improve based on expert feedback.
3. Continuous Feedback Loop: Human experts analyze AI system outputs, providing feedback that helps our AI models constantly improve. This iterative process ensures our AI stays ahead of evolving threats.

Navigating AI Regulations with a Security Mindset

Modern AI systems generally leverage a mix of regulated and unregulated data on systems that may fall into the scope of compliance requirements in sectors that may have their own distinct sets of rules. But there are common requirements to implement security safeguards appropriate to the risk. For example, the General Data Protection Regulation (GDPR) includes many security provisions that are technology-neutral, meaning personal data must be protected under the law regardless of whether or not AI is being used. As new AI regulations emerge, such as the EU AI Act, it’s critical to approach compliance with both privacy and security in mind.

Looking ahead, AI will be a critical element in both complying with data protection requirements and mitigating security and privacy risks. All the while, it is important to continue embracing common data protection compliance regimes, which is why CrowdStrike is certified to global privacy frameworks including the EU-U.S. Data Privacy Framework, the United Kingdom Extension to the EU-U.S. DPF, the Swiss-U.S. Data Privacy Framework, and the APEC CBPR and APEC PRP.  This is also why CrowdStrike contributes to forums designed to inform the future of AI, including as a founding member of the NIST AI Safety Institute Consortium.