The cybersecurity industry is moving fast on AI, but the conversation around value is still nascent.
Nearly every vendor now has an AI story. Most of those stories sound compelling on the surface: faster triage, faster summaries, faster investigation, faster response. But speed alone is not the standard security leaders should use to evaluate AI in the SOC. Faster access to information is not the same as better decisions. More output is not the same as operational value.
That distinction matters because many security teams are already operating under strain. Analysts are overloaded. Tool sprawl is real. Tolerance for added workflow friction is low. At the same time, CISOs are under increasing pressure to modernize operations and show that AI investments are improving security outcomes, not just keeping up with the latest market narrative.
That is why I think many AI conversations in security still miss the point. The question is not whether AI can generate more analysis. It is whether it makes analysts more effective and improves how the SOC actually works.
The Market is Still Collapsing Very Different Kinds of AI into One Bucket
One of the more useful frameworks I have seen recently comes from Enterprise Management Associates, which distinguishes between useful AI, autonomous AI, and productive AI in cybersecurity.
That distinction is worth paying attention to because the market often treats all three as if they create the same kind of value. They do not.
Useful AI can absolutely help. It can enrich alerts, surface patterns, summarize findings, and accelerate routine tasks. But useful AI does not always improve productivity. In some environments, it can actually create more work by generating additional analysis that analysts still have to interpret.
Autonomous AI is a different category altogether. It is appealing in theory because it promises independence and speed. But in real-world SOC environments, autonomy without transparency, guardrails, and human validation can create serious operational risk. If an analyst cannot understand why the system reached a conclusion or recommended an action, that is not just a technical limitation. It is an accountability problem.
Productive AI is the category that matters most. Productive AI does not just add capability. It improves how work gets done. It reduces manual workload, strengthens prioritization, shortens time to triage, and helps teams respond with greater consistency. It fits into real workflows instead of forcing analysts into another layer of interpretation.
That is the bar security leaders should be using.
The Core Mistake is Confusing Speed and Enrichment with Decision Support
A lot of AI in the SOC is still operating at the enrichment layer while being marketed as something more advanced.
There is an important difference between speed and intelligence. Many solutions summarize alerts faster, surface data faster, or make technical content easier to read. Those capabilities are useful. But they still leave the analyst to answer the hardest questions: What matters? What is the likely risk? What should happen next?
Decision support is different. It requires the system to correlate signals, apply context, and help guide action with a level of confidence. It is not just about showing more information. It is about making the information more operationally meaningful.
That is why more output does not automatically create more value. In the SOC, operational value comes from prioritization, context, and actionability.
What Productive AI Looks Like in Practice
Productive AI is not an add-on feature. It is an architectural choice about where AI belongs in the workflow.
When AI is embedded well, it helps connect fragmented signals across identity, endpoint, cloud, and telemetry sources. It helps analysts move from scattered evidence to a coherent investigation narrative faster. It reduces manual effort in case assembly, supports more consistent first-level triage, and helps frame likely severity and next-best actions.
Generative AI also has a useful role to play, but it should be understood clearly. Its strength is not magically turning raw telemetry into truth. Its strength is helping analysts absorb information faster, summarize technical findings, translate technical artifacts into readable context, draft communications, and move through repetitive work with less friction. Its value depends on the quality of the detections, enrichment, and decision logic underneath it.
Response is where this becomes especially important. In a high-functioning model, AI can assemble the full chain of activity around a suspicious event, connect identity and endpoint behavior with cloud access, assess likely severity, and recommend next best actions. But the analyst still validates intent, weighs business impact, and determines whether the recommended action is appropriate in the context of the environment.
That is what productive AI should do. It should make human defenders more effective, not less necessary.
The Better Standard for Security Leaders
Security leaders should stop asking whether a platform uses AI and start asking how that AI improves the operation of the SOC.
- Does it reduce analyst burden, or just repackage the work?
- Does it improve prioritization and decision quality, or simply accelerate access to information?
- Does it fit into the way the SOC already works?
- Does it preserve accountability, transparency, and human validation where they matter most?
- Can the vendor prove measurable operational outcomes in production?
Those questions will matter longer than any current AI label or feature category because they get to the real issue: whether the technology improves security operations in practice.
AI Should Make the SOC Better, not Busier
The future of AI in the SOC will not be defined by how advanced the interface looks or how many tasks a system can automate in isolation. It will be defined by whether AI reduces manual workload, sharpens human judgment, and improves response quality under pressure.
That is the model we believe in at Deepwatch. With NEXA, our collaborative AI ecosystem for MDR, we are focused on using AI to connect context, improve prioritization, and strengthen the way analysts and customers work together. The goal is not autonomy for its own sake. It is a more resilient security operation: one that can move faster, make better decisions, and respond with greater confidence.
For security leaders trying to separate real operational value from vendor hype, the distinction between useful AI, autonomous AI, and productive AI is a good place to start.
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