In an AI-Driven SOC, Trust Is the New Differentiator

The threat intelligence market isn't just changing, but consolidating. But not in the way many expected.

This consolidation isn’t being driven primarily by price pressure, feature parity, or even platform sprawl. It’s being driven by something far more fundamental: trust.

As automation and AI become deeply embedded in security operations, the differentiator is no longer who can ingest the most data or generate the fastest enrichment. It’s who can earn and maintain analyst trust in the intelligence, context, and recommendations being delivered.

In 2026, threat intelligence platforms don’t fail because they lack data. They fail because analysts don’t believe them. In practice, this shows up quietly. Analysts leave automated recommendations untouched. High-confidence alerts get re-investigated manually. “Auto-close” rules are disabled. The system still runs, but no one relies on it.

Why Trust Has Become the Market Axis

Security operations teams are increasingly dependent on automated and AI-assisted decisions. Alerts are triaged automatically. Investigations are accelerated by machine reasoning. Recommendations are generated at machine speed.

The more automation SOCs adopt, the more trust they need in the systems making, or influencing, decisions on their behalf. Security analysts predict that in 2026 some automated security decisions will be reversed due to lack of transparency or confidence in AI-driven recommendations, slowing adoption rather than accelerating it.

Chris Vincent, Chief Commercial Officer at Anomali, described this shift plainly during a recent threat intelligence webinar saying, “Automation and AI are no longer optional. But if teams don’t trust what those systems are doing, they simply won’t use them.”

This is the central tension shaping the threat intelligence market today. Platforms are being asked to move faster, reason more deeply, and act more autonomously, while simultaneously being more transparent, explainable, and accountable.

Black-Box Intelligence Erodes Analyst Confidence

One of the fastest ways to lose trust is opacity.

Many modern intelligence platforms promise AI-driven insight but offer little visibility into how conclusions are reached. Alerts arrive labeled “high confidence.” Recommendations appear fully formed, and scores are presented without explanation.

From a distance, this looks efficient, but in practice, it undermines adoption.

George Moser, Chief Growth Officer at Anomali and a former enterprise security leader, captured this dynamic succinctly:

“If I don’t understand why the system is telling me something, I’m not going to act on it,  — especially when the stakes are high.”

Security analysts are trained skeptics. Their job is to question assumptions, validate evidence, and make defensible decisions. Black-box intelligence asks them to suspend that instinct — and that’s a losing proposition.

In 2026, platforms that cannot explain their intelligence will increasingly be sidelined, regardless of how advanced their algorithms may be.

Analysts Must Understand Why a Recommendation Exists

Trust is not built by accuracy alone. It’s built by understanding.

Operational intelligence today does more than enrich alerts — it recommends actions. That recommendation might involve blocking infrastructure, escalating an incident, or triggering automated response.

For an analyst, the question is not simply “Is this right?” It’s “Why is this the right thing to do?”

Moser emphasized this point during the discussion, noting, “Analysts need to see the reasoning. They need to know what signals were correlated, what assumptions were made, and what confidence exists.”

This is where many intelligence platforms struggle. They focus on outputs instead of reasoning. They optimize for speed without investing in explainability.

In contrast, trusted platforms expose:

• The signals and telemetry behind a conclusion
• The intelligence sources that influenced it
• The confidence level and its rationale
• The tradeoffs involved in the recommended action

This doesn’t slow analysts down. It accelerates decision-making by eliminating doubt.

Automation Increases the Cost of Getting It Wrong

As automation increases, the blast radius of mistakes grows.

In earlier SOC models, intelligence might inform a manual decision. Today, it can directly influence automated workflows. A flawed recommendation can cascade across environments in seconds.

That reality changes how teams evaluate intelligence.

Christian Karam, technology investor and advisor, highlighted this shift:

“When intelligence becomes executable, trust becomes existential. You can’t afford to act on something you don’t believe.”

This is why trust has emerged as a consolidation force. Security leaders are increasingly standardizing on fewer platforms because they need fewer decision engines they can rely on. Platforms that repeatedly surface low-confidence, poorly explained, or irrelevant intelligence will be removed from from critical workflows.

Human-in-the-Loop Is Not a Step Backward

Trust does not require removing humans from the loop. In fact, trust grows when humans remain meaningfully involved as validators and decision-makers. According to NIST guidance on AI systems, human-oversight mechanisms are now considered essential controls for high-impact automated decisions, particularly in security and safety domains.

Pierre Lamy, a long-time threat intelligence practitioner, described this balance during the webinar:

“The goal isn’t to replace analysts. It’s to help them ask better questions and get better answers, faster.”

This is the essence of human-in-the-loop and human-on-the-loop design:

• Machines do the heavy lifting at scale
• Humans retain judgment and oversight
• Systems explain, humans decide

Platforms that attempt to bypass human judgment in pursuit of full autonomy often face resistance; teams likely won't outright reject automation, but will usually reject anything unaccountable. Trustworthy intelligence platforms elevate analysts.  

Trust Is Built Through Consistency, Not Perfection

Another misconception is that trust requires flawless intelligence. Trust is built through consistency: consistent logic, consistent prioritization, and consistent outcomes. Analysts learn systems the same way they learn adversaries. When prioritization logic behaves consistently, teams develop intuition. When it changes silently, they lose it.

Moser pointed out that even highly accurate intelligence can fail operationally if it behaves unpredictably: “If the system changes how it prioritizes threats without explanation, analysts stop trusting it.”

Behavioral studies in decision support systems show that users are more likely to trust slightly imperfect but predictable systems than higher-accuracy systems with opaque or shifting logic. Trusted platforms behave in ways that analysts can learn and anticipate. When they evolve by incorporating new models or new data sources, those changes are visible and explainable.

This consistency allows teams to calibrate their judgment over time. They learn when to lean in, when to question, and when to override — all essential behaviors in a healthy SOC.

Trust as the New Buying Criterion

As a result of all this, buying behavior is changing.

Security operations leaders are no longer asking:

• “How many feeds do you ingest?”
• “How much data can you enrich?”

Instead, they’re asking:  

• “Can my analysts understand and defend these decisions?”
• “Will this platform support judgment, not replace it?”
• “Can we trust this system when it matters most?”

This is where market consolidation accelerates. Platforms that fail to earn trust are quietly deprioritized. Platforms that support transparency, reasoning, and human oversight become central to operations.

Trust becomes sticky. Once earned, it’s difficult to displace.

The Market Is Consolidating Around Confidence

The threat intelligence market is converging around a principle.

In a world of AI-driven security operations, trust is the differentiator.

Black-box intelligence erodes confidence. Explainable intelligence builds it. Platforms that respect human judgment will outlast those that attempt to bypass it.

The future of threat intelligence doesn’t belong to the loudest claims or the most aggressive automation. It belongs to platforms that earn the right to be trusted. Find out how Anomali is evolving threat intelligence to drive trusted decisions and stronger security outcomes.  

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