Generative AI is transforming cybersecurity, enabling real-time threat detection and automated incident response. However, many organizations are discovering that outdated infrastructure quietly limits these advances. When underlying systems lag, even the most sophisticated AI tools cannot perform at their full potential.
While AI platforms have become more capable, their success depends heavily on the surrounding environment. Fragmented networks, inconsistent logging, and outdated identity controls can lead to missed detections, delayed responses, and an overwhelming volume of false positives. These are the types of blind spots that attackers are quick to exploit.
Modern AI requires modern infrastructure
AI security relies on accurate models, integrated telemetry, fast data processing, and responsive enforcement. According to a NTT Data study, 90% of enterprises are reevaluating their infrastructure to prepare for AI adoption. The research highlights how gaps in networking, data platforms, and security tooling are becoming bottlenecks for effective AI deployment. For example, CBTW supported Mobilidee in strengthening access control and compliance through infrastructure modernization. By improving automation and integrating governance into its SmartCockpit platform, Mobilidee laid a stronger foundation for future AI-enabled insights.
Where legacy systems undermine AI defense
Outdated infrastructure limits AI’s potential in three key areas:
· Data silos block visibility. AI security thrives on broad context. When identity, cloud, and endpoint data are scattered across systems, important correlations can be lost.
· Lag delays detection. Effective AI defense depends on low-latency pipelines. Older infrastructure often slows down telemetry and decision-making enough to let threats progress.
· Alert quality suffers. Without clean, well-structured input, AI tools may generate too many alerts or overlook meaningful deviations.
In one cloud-based environment, CBTW helped respond to a crypto-jacking incident detected through abnormal compute usage. While AI-driven detection tools raised alerts, fragmented logging and lack of centralized monitoring delayed the investigation. After enhancing AWS security controls and improving cluster security, the organization improved its incident detection and containment capabilities.
Read the case study → https://cbtw.tech/insights/crypto-jacking-prevention-aws-cloud-security-case-study
Architectural priorities for AI security
Improving AI security starts with addressing foundational architecture. Stronger models alone won’t close the gap unless core systems are aligned to support them.
Focus areas include:
- Implement unified telemetry that spans cloud workloads, devices, and identities for a complete activity view.
- Enable real-time policy enforcement that adapts to the current context and evolving threats.
- Build scalable data infrastructure to support continuous analysis without performance bottlenecks.
- Apply consistent access controls to simplify user management.
A Google Cloud whitepaper reinforces this, noting that legacy systems often create friction and weaken AI’s ability to detect lateral movement or policy violations.
CBTW also worked with a retirement fund to conduct red team testing, including phishing simulations. The exercises revealed that legacy network segmentation and manual access controls allowed easy lateral movement within the environment. After modernizing network segmentation and improving access controls, the organization achieved measurable improvements in threat detection and response capabilities. Read the case study
Bridging the gap with behavioral AI
CBTW’s partnership with Darktrace enables organizations to apply behavioral modeling across cloud services, user accounts, and endpoints. Darktrace’s AI builds baselines of normal activity and flags suspicious deviations, which is especially helpful in environments with limited or fragmented infrastructure.
This adaptive approach provides early warnings for unusual patterns, such as unauthorized tool usage or anomalous data transfers, helping security teams stay proactive even during transformation phases.
AI Success Depends on Strong Systems Architecture
AI holds great promise for security. But when deployed on top of outdated systems, its value is limited. Beyond detection, security teams need infrastructure that enables AI to act with speed, context, and precision.
At CBTW, we help organizations address this challenge directly. From infrastructure modernization and access control design to AI tool integration and threat modeling, we align architecture with the needs of modern defense. Combined with our strategic partnership with Darktrace, we give clients the tools to stay ahead of emerging threats without over-relying on any one technology.
If you’re considering what’s next for your cyber strategy, we’d be happy to share what we’re seeing in the field.
