Dr. Rob Rowe, VP, Data Science, HID, tells Security Advisor Middle East about the importance of being meticulous about data sourcing, ensuring clear and defined purposes for data collection and maintaining transparency with data subjects.
How has the rise of artificial intelligence impacted traditional security measures ?
There are many advantages and challenges with AI, but the analytic capabilities of AI should be seen as the low-hanging fruit to enhance identity management. Identity analytics has the potential to utilize AI to pore over data from a wide range of sources to rapidly bring to light trends and patterns as well as anomalies not visible to the human eye.
Promising use cases include embedding advanced AI machine-learning capabilities into products and offerings or applying AI analytics to identify gaps in internal business processes—whether it’s to optimize performance in applications such as customer service, technical support, etc., or to help detect issues before they become costly problems.
How can AI be utilized to enhance cybersecurity defenses against evolving threats?
Perhaps the most visible application of AI is biometrics, which uses machine learning to identify individuals through facial recognition, fingerprint analysis, and spoof detection. Sophisticated algorithms are crucial for these tasks.
A more sophisticated biometrics approach is in behavioral modeling, which leverages machine learning to identify and analyze behavioral and transaction patterns, enabling proactive detection of anomalies and potential threats. So, machine learning plays a crucial role in this, allowing the security system to learn and adapt to individual baselines.
How can organizations ensure the transparency and accountability of AI algorithms used for security purposes?
AI relies on vast amounts of data, and data presents a significant challenge: bias. When data inherits biases, specific conclusions and outcomes in security systems can be skewed, opening the doors for malicious actors to exploit these biases and bypass security measures. In that sense, AI outputs should be used a guide, not a definitive result.
Beyond addressing data bias, robust and ethical data governance practices are essential. This means being meticulous about data sourcing, ensuring clear and defined purposes for data collection, and maintaining transparency with data subjects.
In what ways can AI contribute to proactive threat detection and prevention strategies?
For example, embedding AI and machine learning directly on edge devices (devices that are located closer to where the data is collected such as a security camera with facial recognition capabilities at airport gates) facilitates real-time anomaly detection and a more efficient response to threats. This shift from reactive to proactive security represents a significant advancement in the field. Many in the security arena already are heading this way, according a recent survey HID conducted with 2,600 end users and industry partners (installers, integrators, and original equipment manufacturers) from across the globe: in addition to analytics, 11% said they are using AI-enabled RFID devices, 15% are using AI-enabled biometrics, and 18% have AI supporting their physical security solutions.
What are the implications of AI-driven autonomous security systems for human oversight and decision-making?
While AI offers great possibilities for security, it’s important to understand that it won’t replace human oversight and decision-making entirely. Security is a complex field, and responsible deployment of AI in this context requires a pragmatic approach. This means AI won’t eliminate people and jobs. Instead, it will act as a powerful tool to help people be more productive, reduce errors and help them identify risks before they occur. So, for tasks requiring human judgment, interaction, or a higher ROI, traditional security practices will remain in place.
