From Data Abundance to Data Relevancy: How AI Is Reshaping the Future of Security

Security teams used to ask for more data. Now, they’re overwhelmed by it.

From video surveillance and door access logs to intrusion detection systems and cybersecurity threat alerts, modern enterprises generate hundreds of thousands of signals each day. According to IBM’s 2024 Cost of a Data Breach Report, the average organization takes 194 days to identify a breach and another 64 days to contain it.

That lag is rarely due to a lack of information. More often, it stems from the inability to cut through the noise and zero in on meaningful threats. When every system is alerting, everything starts to feel equally urgent or equally easy to ignore.

Why More Data Doesn’t Always Equal Better Security

The assumption that more data equals better security has led to a fragmented landscape of tools, dashboards, and sensor inputs across both physical and digital domains. But when every door sensor, badge reader, firewall, and camera feed contributes to an ever-growing volume of alerts, the burden shifts from gathering data to managing it.

Security teams spend valuable time sifting through raw logs and cross-referencing disconnected systems, often without a clear view of what is actionable. As a result, even well-resourced organizations find themselves reacting to incidents too late or misallocating resources to low-risk events.

This abundance of signals without context is where traditional security approaches fall short. A badge swipe at midnight, for example, might not raise flags on its own. But what if that swipe came from an employee not scheduled to be onsite, whose credentials were recently revoked, and whose network login failed minutes earlier?

Individually, each of those data points offers limited insight. Together, they paint a very different picture. Without a system to correlate and prioritize across domains, these types of threat indicators often go unnoticed until it’s too late. Relevancy—not volume—is what turns raw data into insight.

What AI Actually Enables for Security Teams

Artificial intelligence changes the way security teams interact with data by shifting the focus from reactive monitoring to proactive decision-making. At its core, AI is capable of learning what “normal” looks like across systems, like access patterns, login behaviors, and building occupancy trends, and spotting the subtle deviations that rule-based systems miss. This anomaly detection helps uncover early indicators of risk, even in situations that don’t violate predefined thresholds.

AI also introduces event risk scoring, weighing context, urgency, and potential impact to prioritize alerts that warrant human attention. Instead of triaging dozens of false positives, analysts can focus on the few that truly matter. In more advanced applications, AI can even trigger automated responses such as temporarily disabling a credential or notifying a supervisor based on confidence levels and business rules.

Over time, predictive models begin to emerge, identifying patterns that point to evolving vulnerabilities or recurring operational gaps. The result isn’t simply more efficient workflows. It brings a fundamentally more strategic approach to securing people, assets, and information.

Applying AI to Physical Identity and Access Management

While AI is often discussed in the context of cybersecurity, its impact on physical security and specifically physical identity and access management (PIAM) is just as profound. In a modern workplace, employees, contractors, and visitors move between physical and digital spaces constantly. Managing who gets access, when, and under what conditions is complex, especially across large organizations or distributed environments. AI helps simplify that complexity by continuously analyzing access behavior for risk signals.

For example, AI can detect when an employee attempts to access a location they rarely visit or when a badge is used outside of normal hours in combination with an inactive user login. These patterns may not trigger conventional alarms, but they can represent early signs of misuse or credential compromise. AI can also streamline visitor management by automatically cross-referencing guests against watch lists, background checks, and policy requirements, making the check-in process faster, more consistent, and less prone to human error.

Most importantly, AI can correlate access control data with cyber event logs to uncover threats that straddle both domains, providing a unified, real-time view of security posture that traditional systems often fail to deliver.

Doing More with Less: AI as a Force Multiplier for Security Teams

Security teams today are expected to monitor more, respond faster, and manage increasingly complex environments, often without a proportional increase in headcount or budget. AI helps bridge that gap by acting as a force multiplier. It reduces the manual workload that eats up hours of staff time, like combing through video footage, validating access logs, or investigating routine alerts. With AI filtering out noise and surfacing relevant signals, teams can operate with greater focus and speed.

This efficiency also translates into improved incident response. When a threat is identified, AI can assist in narrowing down the timeline, identifying affected systems or individuals, and even recommending next steps based on similar past events. By reducing time-to-decision, organizations can contain incidents more effectively and minimize operational disruption. The result is accelerated, intelligent action that enhances security outcomes despite resource constraints.

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