For years, security investment has been driven by a single instinct: see more. More telemetry, more tools, more dashboards, more alerts. That instinct made sense when threats were noisier and slower.

However, adversaries are now weaponizing AI to move at machine-speed, outpacing the human-led processes many organizations depend on.

The CrowdStrike 2026 Global Threat Report puts it bluntly. The average breakout time (the window between initial access and successful lateral movement or privilege escalation) has dropped to just 29 minutes, with the fastest case recorded at 27 seconds.

In that timeframe, most security teams haven’t even triaged the first alert, let alone determined how to respond.

Why modern attacks are so hard to spot

Breakout time is just one metric in a broader attack chain, and speed isn’t the only thing that’s changed. Attackers are also blending into legitimate access paths and relying less on malware.

According to CrowdStrike, 82 percent of detections are now malware-free. Instead of breaking in, attackers are logging in with valid credentials and hijacking trusted identities to navigate environments. There’s no payload to detonate, no anomalous binary to flag. The activity looks normal because, technically, it is.

This is why traditional, signature-led detection is struggling. The signals that count are either no longer obvious, or they’re unfolding faster than human SOC teams can process.

AI-powered attack patterns live between the seams: an identity behaving slightly differently in one place, a cloud workload reaching somewhere it shouldn’t in another. Joining these dots is now the core job of the security operations center, and it’s significantly harder than it used to be.

The Verizon 2026 Data Breach Investigations Report (DBIR) notes that 20 percent of breaches take months or more to notice, and the IBM Cost of a Data Breach Report 2025 found that it takes organizations an average of 181 days to identify that a data breach has occurred.

How AI is reshaping both the attack and the attack surface

CrowdStrike also reports an 89 percent year-over-year increase in AI-enabled attack activity, with adversaries using generative AI to automate social engineering, accelerate reconnaissance, and craft more convincing phishing at scale. Surprisingly, this understates what is actually happening. Adversaries aren’t just using AI to move faster. They are using it to evade detection.

Modern AI-driven intrusions treat the enterprise like a weighted graph, where the “cost” of a network hop is measured by the probability of getting caught. An autonomous agent can map the environment and deliberately route around endpoint detection and response (EDR) choke points, blend into a specific user’s historical login window, keep exfiltration rates under security information and event management (SIEM) thresholds, and ride entirely on native protocols.

This can increase breakout time from minutes to days, while the probability of detection drops significantly. The attacker isn’t moving faster; they’re choosing a longer, stealthier residency. Empowered by AI, they can quietly scale their capabilities, deploying traps across multiple environments and waiting for the right moment to strike.

At the same time, AI systems themselves are now part of the attack surface. The CrowdStrike report identified a surge in prompt-injection attacks, noting that adversaries successfully compromised enterprise generative AI tools at over 90 organizations.

Ultimately, AI is increasing attacker scale while reducing the probability of detection, and creating new assets to protect.

To counter this shift, security operations teams need to operate at machine speed while capturing the telemetry and signals that current stacks miss. It is critical to understand how activity across identities, endpoints, networks, AI, and cloud systems connect to form a broader threat narrative.

Why yesterday’s SIEM can’t see today’s attacker

Most SIEM environments were originally designed for log aggregation, compliance reporting, and after-the-fact investigation. They were built to help analysts look back. The current threat landscape demands the ability to look across in real time, and act immediately.

The answer isn’t more legacy SIEM infrastructure. It is a different operating model entirely.

Organizations should pivot toward an agentic SOC while keeping humans in or on the loop for critical judgment. By leveraging AI-driven analytics, identity context, and automated response as their primary engine, your security teams can transition to a true AI-driven SIEM.

Industry analysis has been pointing to this shift for some time, but closing the gap between intent and execution is challenging. The issue isn’t a lack of data. It is separating background noise from the signals needed to drive decisive action quickly enough to make a meaningful difference to the end result.

What an agentic SOC looks like, and why AI-driven SIEM is the foundation

The organizations pulling ahead are treating SIEM differently. They see it not as a log repository, but as the operational core of their security strategy.

They are unifying telemetry across endpoint, identity, cloud, and SaaS in one place, and applying AI to correlate and prioritize signals. By automating routine aspects of investigation and response, they’re driving machine-speed execution without sacrificing human judgment.

This is the model CrowdStrike has been building toward. The CrowdStrike Falcon Next-Gen SIEM platform brings detection, identity protection, cloud security, and SIEM analytics together in a single AI-native environment. The goal is to minimize the time between threat detection and response, whether that response is automated or analyst-led.

But technology alone doesn’t deliver that outcome. The hardest part isn’t choosing a platform; it’s operationalizing it.

Where most security programs still get stuck

Even the best platform can’t outperform the environment it’s dropped into. Integration gaps, unclear ownership, inconsistent processes, and unrealistic ingestion strategies erode the value of every tool in the stack. As a result, security leaders are left feeling like they’re running faster without ever closing the gap.

Security operations need a clear architectural approach, a realistic plan for how data flows, and an honest view of what the team can run day to day. That is where the difference between a deployed platform and a defensible organization is really made.

SHI helps close this operational gap. We guide you through licensing, posture analysis, platform adoption, and ongoing optimization. Our focus is to ensure your technology works as intended, and improves technical and business outcomes.

NEXT STEPS: 

Speak with an SHI expert to map out a security operations approach built for your organization’s environment and goals.

SHI’s SIEM Accelerator can help you operationalize solutions such as CrowdStrike Falcon Next-Gen SIEM, bringing people, process, and technology together so your team can move from signal to decision at machine speed rather than fighting integration gaps and alert noise.

Our no-cost Security Posture Review surfaces hidden visibility gaps that slow response times. We also help right-size your licensing, so your budget aligns with your defenses. The result is a consolidated platform experience rather than a stack of tools, allowing you to securely procure, deploy, and scale AI.

If you’re heading to CrowdStrike Fal.con 2026, taking place August 31 – September 3 in Las Vegas, visit the SHI booth.

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