Enterprises are testing whether AI in security operations delivers real returns or just more noise and hidden costs.

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For years, security leaders have been told that artificial intelligence would finally outpace attackers. In that near-utopian world, SOCs — also known as security operations centers — would become faster, leaner and more autonomous. Vendors promised fewer false positives, faster incident response and analysts freed from repetitive triage. Now, after billions spent and a wave of AI-driven tools flooding the market, executives are asking a tougher question: Has AI actually delivered a measurable return for SOCs?

According to the 2025 Pulse of the AI SOC Report by Gurucul and Cybersecurity Insiders, 60 percent of adopters say AI has helped cut investigation time by at least a quarter. While that’s a promising start, it only scratches the surface of what ROI in security really means. For many organizations, the conversation is moving from “How much can AI detect?” to “How much does it actually save?” As I wrote in a previous Forbes article, real value from AI investments often lies in operational resilience and trust, not just bottom-line savings.

What ROI Really Means

Asaf Wiener, CEO and co-founder of Mate Security, which offers a memory-based AI agent platform for SOC investigations, believes the question of ROI boils down to one metric: Speed. “The critical question,” he told me, “is whether your containment is faster than the attacker’s execution time.” In practice, that means asking whether an organization can stop an attack before it pivots or exfiltrates data.

Wiener argues that detection rates are a vanity metric. The real test is in how quickly and consistently a SOC can respond. “If your investigation takes 45 minutes per alert across hundreds of alerts,” he clarified, “the attacker wins the race.” Metrics like the mean time to respond (MTTR), analyst retention, and the rate at which teams learn from incidents all tie directly to ROI. The goal isn’t just to spot threats but also to outpace them.

Fewer Alerts, Same Chaos

Alert fatigue remains one of the most expensive challenges in cybersecurity. AI was supposed to fix that, but many teams report that it has simply reshaped the problem. “You go from 500 generic alerts to 50 high-priority ones that still need full investigation,” Wiener said. “The analyst still opens 12 tools, still manually correlates data, still burns time.”

That frustration echoes across the industry. Adlumin’s 2025 State of the SOC Report describes AI as a “force multiplier” but warns that tools that aren’t tailored to an organization’s environment risk creating new kinds of noise. This results in a costly paradox: Even though security teams detect threats faster, they end up spending more time on analysis.

Wiener says the real gains appear when AI understands context, knowing which user behaviors are routine versus suspicious. “When AI learns that a travelling employee logs in from different time zones or that Friday exports are normal, it can automatically resolve what’s benign,” he explained. “Only then does noise truly disappear.”

As I explored in Starved Of Context, AI Is Failing Where It Matters Most, intelligence without real-world understanding can make AI systems brittle — and in the SOC, that brittleness translates to false positives and wasted analyst time.

The Hidden Costs Undermining ROI

Even as AI-driven SOCs show potential, hidden costs often erode returns. Wiener points to what he calls “the training trap”: Systems that require constant feeding of labeled data for every new use case. “Your team becomes data labellers instead of threat hunters,” he said.

Integration is another obstacle. AI tools that work only with custom data lakes or proprietary infrastructure create new silos, driving up complexity. And then there’s the “black box” risk — when models make decisions no analyst can easily explain. “If analysts don’t trust those decisions, they’ll review everything manually,” Wiener said. “That kills ROI.” As I noted in another Forbes article, systems that fail to learn fast enough end up compounding errors — and in cybersecurity, every delay carries a cost.

Cybersec-Automation’s 2025 analysis makes a similar observation: “Closing alerts doesn’t mean your SOC is getting smarter. What matters is whether your AI agents are helping you improve over time.” The line underscores how oversight and explainability aren’t academic concerns — they’re core to achieving economic value.

The Trust Test

Trust, Wiener said, is the hinge on which SOC automation either delivers or fails. “If analysts don’t trust AI decisions, you’ve just added an expensive tool that slows them down.” He argues that the solution isn’t to drown users in explanation but to redesign how AI surfaces its logic. “Verification should happen in seconds, not minutes,” he noted. “The design should make decision review effortless.”

This approach mirrors how software engineers now use AI-assisted coding: Reviewing the logic points that matter, not every line. When SOC tools can do the same, presenting clear reasoning that humans can verify instantly, AI shifts from an assistant to a true force multiplier. Oversight then becomes a performance enhancer rather than a hindrance to speed.

CFOs, Boards And The ROI Conversation

While CISOs and CFOs speak different languages, Wiener believes they ultimately want the same thing: Business velocity. “The CFO isn’t drilling into cost-per-investigation metrics,” he said. “They’re asking whether AI security investments enable the business to move faster, to launch products sooner, expand into new markets, or take smarter risks.”

That framing is gaining traction across the industry. Abnormal Security notes that AI-driven insights are now “directly improving board-critical metrics such as mean time to detect and mean time to respond,” linking operational performance to business outcomes. IBM’s 2025 X-Force Threat Intelligence Report similarly found that AI and automation helped organizations cut average breach containment time by 108 days — a direct improvement that boards now cite as proof of return on investment.

The Bottom Line

In the end, the ROI of AI in security won’t be decided by detection rates or glossy dashboards. It will come down to how effectively companies can balance speed, trust, and oversight. As Wiener puts it, “The AI tools that win will be the ones that help defenders finally win the race — faster containment, greater agility and measurable business impact.”

AI hasn’t made the SOC fully autonomous yet. But for companies willing to rethink how they measure value — and how they build trust into the new world of automation — the payoff may finally be within reach.