Staying Ahead of Security Blind Spots in the Age of AI
Organisations are rapidly integrating artificial intelligence (AI) into their operations. According to a recent study by McKinsey, 78% of organisations are using AI within at least one business function. In parallel, AI adoption is accelerating at an extraordinary pace, with global investments forecasted to exceed $300 billion by 2025.
As employees seek faster and smarter tools to support them in their roles, and organisations look for ways to close the current skills gap, increase productivity and accelerate innovation, AI tools have become an essential resource for enterprises. However, the rapid rise of AI has also led to a surge in unauthorised AI use, known as shadow AI, raising concerns that this will expose individuals and organisations to significant risk and vulnerabilities.
When employees deploy AI tools without oversight from security and IT teams, the risk of unintended data leakage increases significantly. Compliance with regulations like GDPR, DORA, and HIPAA relies on having comprehensive visibility into how data is accessed, processed, and shared. As AI adoption accelerates, the rise of shadow AI undermines this visibility and significantly increases the risks of non-compliance.
Today’s security leaders are operating in an environment that’s more distributed, more dynamic, and harder to control. AI workloads are introducing unprecedented traffic patterns and explosive data growth. According to the Gigamon Hybrid Cloud Security Survey, 1 in 3 organisations report that network data volumes have more than doubled in the past two years due to AI. Traditional security tools that rely on MELT (metrics, events, logs, and traces) data, weren’t designed to deal with this volume and level of complexity. Consequently, they struggle to detect AI-driven threats and the result is a significant increase in blind spots across these complex environments.
AI tools also expand the organisational attack surface. Corporate LLMs have increasingly become a lucrative target for threat actors, with 47% of organisations now reporting an increase in attacks targeting their AI/LLM deployments. This growing threat landscape is compounded by the complexity of hybrid cloud environments, where AI workloads often span multiple platforms. As corporate LLMs become more deeply integrated into business operations, the consequences of a successful breach, ranging from intellectual property theft to model manipulation, can be severe and far-reaching.
When compromise becomes the new norm
Pressured to deploy AI systems as quickly as possible, the real challenge for organisations lies in managing how AI is deployed internally, how sensitive data is being used, and how security operations can keep pace. Whilst every business claims that security is a priority, 91% of security and IT leaders admit they’re making significant and potentially dangerous compromises in their security strategies amid the AI surge. Teams are forced into making difficult decisions, prioritising agility and speed over visibility, often having to sideline clean, high-quality data to support new AI deployments and integrating complex environments faster than they can be secured.
The most significant trade-off for security leaders is insufficient visibility of data in motion across networks. A lack of insight into AI-generated traffic, whether it’s sanctioned or unsanctioned, including apps, workloads, and large language models, makes effective governance impossible and exposes the organisation to serious risks, including non-compliance and data poisoning.
Staying ahead in the age of AI
One thing is clear—this new age demands a different approach. To stay ahead, CISOs should prioritize an offensive vs. defensive strategy, reassessing the risks tied to AI, and placing visibility at the centre of everything that they do. To combat shadow AI, one in three CISOs are now implementing guardrails around the usage of large language models. Metadata has also emerged as essential to securing AI deployments and making existing security tools more effective. The key for CISOs is having a single source of truth that reveals what is really happening across hybrid cloud environments. Traditional telemetry is no longer enough. As AI workloads proliferate, organisations require comprehensive visibility and actionable insights to reduce risk, improve governance and address compliance gaps. The security and observability tools in use today are not broken, they are just ineffective because they often lack precise network insights. By combining network telemetry with application-level context and metadata, security teams can augment their existing tools, detect previously unseen threats, accelerate the resolution of performance issues, and shine a spotlight on hidden vulnerabilities.
Fighting AI with AI: The Next Step in Visibility
The clearer and deeper the picture, the faster security teams can identify root causes and defend against the new wave of AI-powered attacks. That’s why the next evolution of visibility is being driven by AI itself. Agentic AI is supporting security teams by fusing network-derived telemetry with insights, meaning analysts can interact with trusted metadata directly, gaining context-rich insights and instant guidance without the need for manual analysis of dashboard metrics.
For security teams, this means spending less time searching for the needle in the haystack, dramatically increasing time to resolution. For CISOs, it means enhancing their team’s capabilities with instant actionable insights, overcoming resource constraints and closing critical skills gaps—all critical for staying ahead in this new era of AI.
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