Artificial Intelligence and Machine Learning
Palo Alto Networks’ Meerah Rajavel on Securing Businesses with Precision AI
Cybersecurity professionals often face considerable challenges – from managing large streams of data – such as logs, endpoint detection systems and firewalls – to integrating numerous security tools. cybersecurity. The focus now needs to be on streamlined processes, artificial intelligence-based solutions and unified architectures.
See also: AI-Driven SOC Transformation with Cortex XSIAM
In an interview with Information Security Media Group, Meerah Rajavel, CIO of San Francisco-based Palo Alto Networks, highlighted why real-time responsiveness and the integration of AI-based technologies are crucial to mitigating modern security threats. cybersecurity. “Security is a problem that can only be solved if you look at it from a perspective of complexity to clarity,” she said.
Rajavel is responsible for Palo Alto Networks’ global information technology functions, leading a global strategy to rapidly scale and deliver innovative solutions to teams around the world.
Edited excerpts follow:
What impact has Precision AI had on solving cybersecurity challenges since its launch, and how does it differentiate itself in solving these problems?
To put things in context, Precision AI represents the evolution of AI in cybersecurity, with machine learning and deep learning techniques being an integral part of many solutions. We’ve been using AI in our products for years. For example, we block more than 11.3 billion attacks daily, 2.3 million of which are brand new threats. Identifying these unique threats on a daily basis requires advanced AI capabilities, particularly deep learning and ML.
When generative AI emerged, it brought new opportunities and challenges. We believe that cybersecurity requires 100% accuracy. Precision AI was developed to leverage the combined strengths of ML, deep learning and AI generation to deliver exceptional accuracy and efficiency. For security professionals, one of the biggest challenges is complexity. Precision AI simplifies this by delivering actionable insights directly to their fingertips, helping them sift through huge volumes of data and threats with context-specific analysis. This technology is integrated into our platforms, including Strata for network security, Cortex for security operations and Prisma for cloud security, ensuring comprehensive protection.
How is Palo Alto approaching AI in cybersecurity? What are the key cybersecurity maturity requirements for effective implementation?
We approach AI in cybersecurity from three distinct vectors. First, securing AI by design is crucial as our customers increasingly rely on AI in their ecosystems. As a cybersecurity solutions provider, our goal is to ensure our customers are protected when using new technologies. The second vector involves fighting adversaries who use AI to launch attacks. The pace of these attacks is exponentially faster and more sophisticated than ever before. To counter this, we need to use AI to protect against AI-based attacks. The third vector focuses on how AI can benefit security practitioners. By simplifying the analysis of complex data and improving interactions between products, AI can significantly improve the efficiency and effectiveness of security operations.
Solutions such as AI Access Security, which provides visibility into the use of AI within businesses and ensures that secure AI applications have been developed across 100 customers already benefiting from our AI security solutions, we are seeing a clear shift in maturity levels. Organizations don’t need to be at an advanced level to get started. Our platforms adapt to their specific needs and evolve with them. With over 10,000 models and 200 dedicated R&D engineers, we are continually improving our capabilities to stay ahead of emerging threats. Our Cortex platform includes more than 4,000 models. Processing 7.6 petabytes of data and 59 billion events daily, our AI-powered solution reduces noise and identifies approximately 100-110 actionable incidents. This allows our customers to focus on high priority threats while automating repetitive tasks.
Accountability in AI governance is a complex issue. How do you see it evolving, particularly as systems become more autonomous?
Governance and accountability are critical areas that require clarity. For example, in discussions with an insurance CISO, we looked at scenarios where autonomous systems make errors. If a system processes a claim incorrectly, who is held responsible? Is it the technology, functional owner or designer of the product? Today, responsibility is usually given to the functional manager. But as AI becomes more autonomous, governance models will need to evolve to clearly define roles and responsibilities.
Transparency and explainability in AI are essential. Large language models pose challenges due to their broad access capabilities. This is why agentic architecture is gaining ground: it allows for higher security constraints and better governance while solving the transparency problems inherent in AI.
How have companies adopted autonomous SOCs in their efforts to achieve autonomy?
Autonomous SOCs are becoming a reality, driven by two key factors. First, adversaries are evolving at a pace that exceeds our capacity to evolve our human resources. Second, there is a shortage of qualified cybersecurity talent. These dual pressures on supply and demand require technological intervention. For example, our XIM – or Extended Incident Management – solution, which we launched just two years ago, has quickly become our fastest growing segment. Its revenue is approaching $1 billion, a testament to its rapid adoption and effectiveness. While solutions like SASE take longer to implement, XIM provides immediate and visible results, meeting urgent business needs.
With AI taking center stage, do you think cybersecurity is being pushed aside? Or is it now getting as much attention as AI?
This is not a situation of choice. If anything, AI has increased security concerns more than ever cloud adoption. The speed at which AI operates and its potential impact have made businesses more attentive to cybersecurity. Boards of directors are now prioritizing cybersecurity discussions because the economic stakes are high. Incidents such as the breaches at UnitedHealthcare, which cost $2 billion, and at Marathon Oil, highlight the disruptive financial consequences of cyberattacks. It’s no longer just about compliance or technology, but about protecting the bottom line. Governments are also enforcing stricter compliance regulations, such as reporting violations within specific deadlines. This regulatory pressure, combined with economic risks, ensures that cybersecurity remains a priority, regardless of advances in AI or cloud technology.
