Transforming Cybersecurity: The Role of AI in Enhancing Threat Detection, Compliance, and Security Across Multi-Cloud Environments

CXOToday has engaged in an exclusive interview with Amit Patil, Senior Director- Technology, Publicis Sapient

How is AI-driven behavioral analytics changing the landscape of insider threat detection in multi-cloud environments?

Insider threats are one of the most significant risks organizations face today, particularly in complex multi-cloud environments where data and operations are spread across different platforms and providers. Traditionally, detecting insider threats has been reactive, relying on post-incident analysis. However, AI-driven behavioral analytics enables a shift towards proactive threat detection.

By continuously monitoring user behavior across multiple cloud environments, AI can identify anomalies in real-time—such as unusual access patterns or actions that deviate from typical behavior. This allows security teams to identify potential threats early, even before they manifest as malicious activities. The integration of AI with Zero Trust principles further enhances this approach by ensuring that every access request, whether internal or external, is rigorously verified, reducing the risk of both external and internal threats.

How can AI and ML support data privacy and compliance in multi-cloud settings across various regulatory jurisdictions?

Looking at the current global business landscape, organizations must navigate a complex web of regulations, such as DPDPA in India, GDPR in Europe, CCPA in California, and various other regional laws, while managing their data across multiple cloud environments. This is where AI and ML can be transformative. By utilizing AI-powered compliance tools, organizations can automate the process of monitoring and ensuring data privacy standards are met across different jurisdictions. Machine learning models can analyze vast amounts of data in real time, flagging non-compliant activities, such as improper data access or unauthorized transfers, based on the rules of each regulatory framework.

Furthermore, AI can help automate the classification of data, ensuring that sensitive data is always stored and processed in compliance with regional laws. This not only reduces the risk of costly data breaches but also builds trust with customers by demonstrating a proactive approach to data privacy and security.

What strategies are key to responsibly leveraging Generative AI for enhancing security in multi-cloud systems?

Generative AI holds tremendous potential in cybersecurity, especially for automating complex security tasks, such as threat modelling, incident response, and even generating secure code. However, as with any powerful tool, its use in security must be approached with caution. One of the key strategies is ensuring proper governance. Before deploying Generative AI models, organizations should implement stringent data governance protocols to ensure that AI does not inadvertently expose sensitive data or generate harmful outputs. It is also essential to establish clear guidelines on ethical usage to prevent misuse or bias in the AI’s operations. AI’s ability to simulate threats or generate test data can enhance security measures, but it’s critical that human oversight remains central to these processes.

Responsible AI usage includes reviewing generated security models and conducting regular audits to ensure that these systems continue to meet evolving security needs without compromising privacy or integrity.

Which AI investments are proving most impactful for boosting cybersecurity within multi-cloud environments?

In multi-cloud environments, one of the most impactful AI investments has been in the area of threat detection and response. AI technologies such as machine learning-based anomaly detection are proving to be invaluable for organizations that operate across multiple cloud platforms. By continuously analyzing network traffic, user behavior, and access patterns in real time, AI can detect even subtle signs of a potential security breach. This capability helps organizations quickly respond to threats, reducing the window of vulnerability.

Another significant area of AI investment is in automated incident response. With AI, organizations can automate many aspects of their response process, such as isolating compromised systems or blocking suspicious activity. This not only speeds up response times but also frees up human security professionals to focus on more strategic aspects of threat management. When combined with Zero Trust security models, AI-driven tools become even more effective, creating a comprehensive defense system that protects critical data across all cloud environments.

What unique advantages does a multi-cloud approach offer for deploying AI/ML models in cybersecurity?

A multi-cloud approach offers several advantages when deploying AI and ML models for cybersecurity, primarily in terms of flexibility, resilience, and scalability. By distributing workloads across multiple cloud environments, organizations can ensure that AI and ML models are able to operate in the most optimal conditions, with each cloud provider offering different strengths in terms of computing power, security protocols, and geographic reach. This diversity not only enhances the performance of AI models—such as those used for threat detection or anomaly analysis—but also helps mitigate the risks associated with a single point of failure. For instance, if one cloud provider experiences an issue, the models can be redirected to other cloud environments without compromising security or performance.