AI-Driven Cyber Threats: Why Visibility is the Next Big Security Priority

As artificial intelligence continues to dominate technological advancements, its darker implications for cybersecurity are becoming more pronounced. While deepfakes may capture headlines, industry experts argue that the real threat lies in AI’s ability to scale cyberattacks, enhancing both their volume and sophistication.

Shane Buckley, CEO and President of Gigamon, and Chaim Mazal, Chief Security Officer at Gigamon, warn that AI is not just making cyberattacks more frequent but also significantly more dangerous.

AI-Powered Attacks: A Growing Concern

“AI is currently up-levelling the capabilities of even novice attackers to execute more advanced tactics and rapidly discover low-level exploitation techniques. As such, organisations need to pay more attention to how to combat the volume of attacks spurred on by AI,” says Mazal. His concerns are backed by data — Close to half of security and IT leaders in APAC report an increase in AI-driven scams, highlighting the pressing need for proactive security measures.

Traditional cybersecurity solutions are struggling to keep up as attackers leverage AI to automate and refine their tactics. With AI lowering the barrier to entry for cybercriminals, the challenge for businesses is no longer just about defending against individual threats but managing an overwhelming wave of increasingly sophisticated attacks.

The Critical Role of Visibility

Buckley emphasises that visibility into AI processes and data flows will be a top cybersecurity priority in 2025. “AI is masking today’s biggest cybersecurity threat to organisations—visibility—which will make it a priority in 2025. Visibility to what is going in and out of AI models and tools will become a must-have this year.”

As businesses accelerate their adoption of hybrid cloud infrastructure, the need for security oversight grows. Many organisations rely on third-party AI tools to manage their cloud environments, yet these same tools introduce new risks. Open-source AI platforms and large language models (LLMs) depend on vast datasets to function effectively, but they are increasingly vulnerable to manipulation.

“With incidents of data poisoning and model inversion on the rise, I predict this is just the beginning,” Buckley warns. Attackers are finding ways to tamper with the data fed into AI models, skewing results and potentially compromising business decisions.

A Call for Deep Observability

Beyond just preventing attacks, organisations must also ensure the integrity of the data driving their AI-powered decisions. “Until organisations have a complete view of all network traffic—both North to South (inbound and outbound) and East to West (lateral traffic within the network)—they remain vulnerable,” says Buckley.

Encouragingly, awareness of this issue is growing. According to Gigamon’s findings, 83% of security leaders in Singapore report that their boards are discussing deep observability as a priority. This signals a shift towards a more comprehensive approach to cybersecurity, where businesses recognise that visibility is fundamental to securing their hybrid cloud environments.

The Path Forward

AI is revolutionising business operations, but it also presents one of the greatest cybersecurity challenges of our time. To stay ahead, organisations must shift from a reactive to a proactive security stance, ensuring they have the right tools to monitor, secure, and manage their AI-driven environments.

By prioritising deep observability, businesses can not only mitigate cyber risks but also safeguard the integrity of their data and decision-making processes. As AI continues to evolve, so too must our approach to cybersecurity—because in this new landscape, visibility is everything.