Fraud Management & Cybercrime
,
Ransomware
Security Leaders Need Deep Observability to Balance Innovation and Risk
Artificial intelligence is fast being embedded into the fabric of modern business. From automating decisions to optimizing operations, AI innovation is moving at a transformative pace, exposing critical gaps in visibility, governance and control.
See Also: Beyond Replication & Versioning: Securing S3 Data in the Face of Advanced Ransomware Attacks
Nearly 80% of companies worldwide are using AI in at least one business function, creating new challenges for security teams. Security professionals are expected to move faster, cover more ground and reduce risk across rapidly evolving modern hybrid cloud environments.
This World AI Appreciation Day, it’s time to challenge the assumption that rapid innovation always comes at the cost of security. Security leaders have an opportunity to redefine security strategies. The goal is balancing the transformative power of AI with a robust governance model.
Achieving this balance requires gaining deep observability into all data in motion across organizations. Traditional monitoring tools weren’t built for the complexities of modern AI workloads. They also weren’t designed for the emergence of shadow AI applications.
Organizations can extend their investments in monitoring tools by optimizing them with network-derived telemetry. This telemetry comes in the form of packets, flows and metadata, helping organizations keep pace with today’s AI innovations.
Deep observability fuses traditional log data from existing tools with network-derived telemetry. This fusion brings the complete picture into focus and provides granular insights into all data in motion across networks. Insights include not only the lateral east-west traffic where attackers often hide but also encrypted traffic and flows across hybrid and multi-cloud environments. Security teams gain complete visibility into what’s happening across the network, detecting threats and anomalies traditional security tools might otherwise miss.
AI Innovation Impacts
Enterprises are racing to harness the benefits of AI, but they’re encountering a new set of risks. One risk – emerging as a top concern – is shadow AI, including the use of unsanctioned tools, employee-procured models and developer-led deployments.
AI workloads are driving an unprecedented spike in network traffic volumes. The 2025 Hybrid Cloud Security Survey found that one in three organizations has seen traffic volumes double over the past two years because of generative AI adoption.
This surge in traffic is creating more complexity and introducing blind spots. Security teams are challenged to keep pace with these rapid changes.
For enterprises to realize the time-to-value they hope to achieve with AI adoption, they’ll need to build robust organizational policies and governance. This is especially important for large language models. Without such guardrails, AI poses an unacceptable business risk, threatening customer trust, regulatory compliance and long-term security.
New Threats in the AI Era
Cyber adversaries are increasingly using AI to hone their skills and power their attacks. Rising breach numbers show that criminals are becoming more successful at using these tactics.
Breaches increased 17% over the past year, affecting 55% of organizations across the globe. These attacks continue to increase in scale and sophistication as attackers mobilize AI to automate their activities. A majority of security stakeholders – 58% – have seen an uptick in AI-powered ransomware attacks. Nearly half – 47% – have seen an increase in attacks targeting AI or LLM deployments. Volumes of phishing and social engineering attacks and the incidence of AI-driven malware and network exploits are also rising.
Turning the tide requires complete visibility to prevent attackers from concealing their presence in the deluge of traffic generated by AI workloads.
Security at the Speed of Business
The democratization of AI has led to an explosion of experimentation across modern hybrid cloud infrastructure. Traditional security strategies and tools were never designed to support rapid business transformation. They certainly weren’t built to evolve at the velocity of AI.
Far too many of today’s security teams are still thinking in terms of years or quarters – not hours. The key to secure innovation is embedding CISOs into the AI framework development from the start.
Security can’t be bolted onto new operational workflows after the fact. It needs to be integrated into their design from the beginning.
Balancing security and innovation means closing visibility gaps and streamlining governance models. It also requires integrating security into organizational cultures. This demands stronger relationships between CISOs and boards.
The Path Forward: Real-Time Insight and Deep Observability
Secure AI adoption demands precision. Today’s security strategies must have the ability to keep pace with fast-changing deployments, growing complexity and increasing traffic. Security leaders must shift their mindset. Instead of asking “How do we keep up?” they should ask, “How do we lead?”
The transformation starts by embedding security into every stage of AI deployment – from experimentation to production. It also means building stronger bridges between security, DevOps and the boardroom. Deep observability supports this alignment by providing actionable network intelligence. All stakeholders can understand and trust this intelligence.
This approach explains why 88% of security leaders agree that deep observability is critical for securing AI deployments. On this World AI Appreciation Day, we need to challenge the notion that security must stifle innovation.
With modern technologies supporting their cybersecurity strategies, CISOs can secure AI at the speed of business.
Ready to learn more?
Download the Gigamon 2025 Hybrid Cloud Security Survey to discover more about how security leaders are balancing the risks and benefits of AI adoption.