AI in the SOC: The ROI Imperative

For years, the cybersecurity industry has been captivated by a vision: artificial intelligence, the digital sentinel, standing guard at the gates of our networks, outmaneuvering attackers with unparalleled speed and precision.

We were promised a near-utopian world where Security Operations Centers (SOCs) would become lean, mean, autonomous machines, free from the drudgery of false positives and endless triage.

Analysts, once bogged down in repetitive tasks, would be elevated to strategic threat hunters, empowered by AI to focus on the truly complex challenges.

Billions have since been poured into this promise, and the market is now awash with AI-driven tools.

Yet, a palpable shift is underway, as executive suites and security leaders alike begin to ask the tougher, more pointed question: has AI actually delivered a measurable return on investment for the SOC?

The initial reports offer a glimmer of hope, though it’s far from the full picture.

The 2025 Pulse of the AI SOC Report by Gurucul and Cybersecurity Insiders suggests that 60 percent of adopters have seen investigation times cut by at least a quarter.

While certainly a positive step, it merely scratches the surface of what true ROI in security entails.

The conversation, it seems, is evolving past the quantitative measure of “how much can AI detect?” to the more critical, qualitative assessment of “how much does it actually save, and how much faster does it make us?”

As I’ve observed in previous analyses, the real value derived from AI investments often manifests not just in direct bottom-line savings, but in the less tangible yet profoundly impactful realms of operational resilience and trust.

Asaf Wiener, CEO and co-founder of Mate Security, a company specializing in memory-based AI agent platforms for SOC investigations, cuts directly to the chase.

For him, the elusive question of ROI boils down to one irrefutable metric: speed.

“The critical question,” he recently told me, “is whether your containment is faster than the attacker’s execution time.”

This isn’t about bragging rights over detection rates, which Wiener dismisses as a “vanity metric.”

It’s about the brutal reality of the cyber battlefield: can an organization halt an attack before data is exfiltrated, before systems are irrevocably compromised, before the attacker pivots and entrenches themselves deeper within the network?

“If your investigation takes 45 minutes per alert across hundreds of alerts,” he clarified, “the attacker wins the race.”

True ROI, therefore, is intrinsically linked to metrics like mean time to respond (MTTR), the often-overlooked cost of analyst retention, and the systemic capacity of teams to learn from incidents and prevent recurrence.

The objective is not simply to spot threats, but to decisively outpace them.

One of the most persistent and expensive challenges in cybersecurity has been alert fatigue.

AI was heralded as the definitive cure, yet for many teams, it has merely reshaped the problem rather than solving it.

“You go from 500 generic alerts to 50 high-priority ones that still need full investigation,” Wiener explains, painting a familiar picture of frustration.

“The analyst still opens 12 tools, still manually correlates data, still burns time.”

This sentiment resonates widely.

The Adlumin 2025 State of the SOC Report, while acknowledging AI as a “force multiplier,” warns that tools not precisely tailored to an organization’s unique environment risk generating new forms of noise.

This creates a costly paradox: security teams might indeed detect threats faster, but the subsequent manual analysis often consumes more time and resources.

The true gains, Wiener argues, emerge when AI transcends mere pattern recognition and begins to understand context.

When an AI system learns that a traveling employee routinely logs in from various time zones, or that large data exports on a Friday afternoon are standard practice for a particular department, it can autonomously resolve what is benign.

“Only then does noise truly disappear,” he asserts.

Without this real-world understanding, as I’ve explored in past writings, AI systems become brittle, leading to a cascade of false positives and wasted analyst hours.

Even when AI-driven SOCs show genuine promise, hidden costs frequently erode the potential returns.

Wiener highlights what he terms “the training trap”: systems that demand a constant, laborious feeding of labeled data for every new use case.

“Your team becomes data labellers instead of threat hunters,” he laments.

Integration presents another formidable hurdle, as AI tools that demand custom data lakes or proprietary infrastructure often create new silos, escalating complexity and cost.

And then there’s the notorious “black box” risk—when AI models make decisions that analysts cannot easily explain or understand.

“If analysts don’t trust those decisions, they’ll review everything manually,” Wiener warns.

“That kills ROI.”

As I’ve previously noted, systems that fail to learn quickly enough end up compounding errors, and in cybersecurity, every delay carries a tangible cost.

The Cybersec-Automation 2025 analysis reinforces this, stating, “Closing alerts doesn’t mean your SOC is getting smarter. What matters is whether your AI agents are helping you improve over time.”

This underscores that oversight and explainability are not abstract academic concerns; they are fundamental to achieving economic value.

Trust, Wiener insists, is the hinge upon 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.”

The solution, he posits, isn’t to overwhelm users with exhaustive explanations, but to intelligently redesign how AI surfaces its logic.

“Verification should happen in seconds, not minutes,” he notes.

“The design should make decision review effortless.”

This mirrors the evolution of AI-assisted coding, where engineers review critical logic points rather than every line of code.

When SOC tools can achieve this—presenting clear, verifiable reasoning that humans can instantly grasp—AI transforms from a mere assistant into a genuine force multiplier, with oversight becoming a performance enhancer rather than a drag on speed.

Ultimately, while CISOs and CFOs often speak different dialects, Wiener believes their core objective is identical: business velocity.

“The CFO isn’t drilling into cost-per-investigation metrics,” he explains.

“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.”

This reframing is gaining considerable traction.

Abnormal Security points out that AI-driven insights are now “directly improving board-critical metrics such as mean time to detect and mean time to respond,” forging a clear link between operational performance and overarching business outcomes.

Similarly, IBM’s 2025 X-Force Threat Intelligence Report found that AI and automation helped organizations slash average breach containment time by an impressive 108 days—a concrete improvement that boards are increasingly citing as tangible proof of investment return.

The true ROI of AI in security will not be determined by flashy dashboards or inflated detection rates.

It will be forged in the crucible of real-world operations, by how effectively companies can balance speed, trust, and intelligent oversight.

AI has yet to usher in the fully autonomous SOC, but for organizations prepared to rethink how they measure value—and, crucially, how they embed trust into this burgeoning world of automation—the promised payoff may finally, decisively, be within reach.

As Wiener aptly summarizes, “The AI tools that win will be the ones that help defenders finally win the race—faster containment, greater agility and measurable business impact.”