The usual response is to add another control, platform or specialist workflow. Yet, 77% of organizations still lack the foundational data and AI security practices needed to safeguard critical models, data pipelines and cloud infrastructure.

Each decision may be reasonable in isolation. Collectively, however, they can create a security estate that is costly to operate and difficult to understand.

Data sits in different consoles. Policies are applied inconsistently. Alerts lose context as they move between systems and teams. Skilled practitioners spend time correlating signals and maintaining tools instead of making higher-value risk decisions.

For resource-constrained organizations, this is more than an efficiency problem. It creates a control problem. Leaders may have broad technology coverage but still lack a reliable answer to three basic questions: What matters most? Where are we exposed? How quickly could we recover?

The security reality gap therefore cannot be closed through tools alone. It requires a connected operating model that combines visibility, governance, identity, automation and human judgment.

AI makes this fragmentation more consequential. A generative AI application may draw on sensitive data, an agent may be assigned access rights and an automated workflow may take action across multiple systems. If identity, data, cloud and application security are managed separately, organizations can struggle to see how these interactions create risk across the whole business.

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The security reality gap: more tools, less confidence