# AI in SOCs Hits a Reality Check: Why 90% of Security Teams Are Underwhelmed


The enterprise security industry is experiencing a sobering moment. After eighteen months of rapid AI adoption, with billions of dollars flowing into AI-powered SOC platforms, agentic tools, and AI co-pilots embedded throughout the security stack, the results are disappointing. A major industry benchmark released in May 2026 reveals that only 10% of Security Operations Centers report "excellent value" from their AI investments, while 71% report marginal or no value at all. The gap between adoption momentum and tangible outcomes signals a structural problem with how AI is currently being deployed in security operations.


## The Adoption Boom vs. The Value Problem


The data paints a stark picture of misalignment. According to the SOC-CMM 2026 Maturity Report—drawn from survey data across approximately 200 SOCs of varying sizes, sectors, and delivery models—AI adoption has accelerated across every category:


| AI Category | Year-over-Year Growth |

|---|---|

| Off-the-shelf Large Language Models | +55% |

| AI Co-pilots | +145% |

| AI Agents | +118% |

| Supervised Machine Learning | +96% |

| Customized LLMs | +64% |


"This is not a market hesitation story," the report's findings suggest. SOC leaders are committed to deploying AI, and budgets are flowing. Yet the same organizations reporting record AI adoption are simultaneously reporting that these investments aren't delivering promised value.


The breakdown of perceived value is telling:

  • 10% report excellent value
  • 19% report good value
  • 71% report some value or none at all

    • That 71% figure represents a structural signal that the first wave of AI in security operations is fundamentally misaligned with how SOCs actually work.


    ## The Taker vs. Shaper vs. Builder Gap


    The survey data reveals a dominant adoption pattern that correlates directly with disappointing outcomes. SOCs fall into three categories based on how they deploy AI:


    Takers (65% of respondents): Organizations deploying off-the-shelf AI tools without customization—SIEM AI triage, EDR AI investigation, SOAR AI playbook generation—essentially bolt-on features. This is the largest cohort and simultaneously the cohort reporting the least value.


    Shapers (20%): Organizations customizing their AI deployments to fit their specific environment and workflow.


    Builders (15%): Organizations investing in custom model training against their own operational data.


    The uniformity of poor value perception across this distribution is striking. Whether a SOC operates as a hybrid model, in-house shop, or managed security service provider (MSSP), the results are nearly identical. The value gap cuts across region and sector. That consistency is the diagnostic clue: the problem is structural, not situational.


    ## Why the First Wave Failed: The AI Feature Problem


    The current AI implementation model treats each tool in the security stack as an independent opportunity for AI enhancement. A SIEM gets AI-powered alert triage. An EDR platform gets AI-driven investigation assistance. A SOAR platform receives AI playbook generation. A ticketing system gets AI summarization. Each feature works in isolation. Each one accelerates a specific slice of the security workflow.


    But here's the critical failure: These AI systems don't talk to each other.


    In practice, SOC analysts now have five AI assistants instead of one unified intelligence layer:


  • The triage agent in the SIEM doesn't know what the detection engineer silenced last week
  • The threat hunting agent in the EDR doesn't know what the threat intelligence team flagged that morning
  • The summarization agent in the ticketing tool doesn't know what the investigation surfaced two hops ago
  • The playbook agent in the SOAR platform has no visibility into the context from upstream alerts

    • Each AI accelerates its own narrow domain. None addresses the handoffs between domains—which is precisely where most SOC time, friction, and value actually lives. A SOC analyst still spends the majority of their shift context-switching between disconnected tools and AI assistants that cannot share critical information.


    ## The Maturity Gap


    The survey uncovered another critical signal: when asked about their biggest operational challenges, SOC teams identified lack of best practices and increased complexity as the fastest-growing pain points.


  • Lack of best practices: +17% year over year
  • Complexity of increasing maturity: +11% year over year

    • Notably, budget constraints and management support—traditional barriers to security investment—both declined. SOC leaders aren't telling the survey they can't afford AI. They're telling the survey they don't know what they're supposed to do with the AI they've already bought. That is the AI maturity gap distilled into two data points: leaders have deployed the tools but lack operational frameworks to extract value from them.


    ## What the Next Wave Must Deliver


    The second generation of AI in security operations will need to address three core failures of the first wave:


    1. Unified Context Across the Security Stack

    Rather than isolated AI features bolted onto each tool, the next generation must create a continuous intelligence layer where context flows seamlessly. If the SIEM's triage agent identifies a suspicious lateral movement pattern, that context must automatically inform the EDR's investigation assistant, the threat hunting agent, and the incident response workflow. The AI system must be the SOC's memory, not a separate assistant for each tool.


    2. Customization and Maturity Frameworks

    The 65% of SOCs still operating in "taker mode" need practical guidance on how to transition toward customization. This means vendors must ship not just AI tools, but operational playbooks, best practices, and migration frameworks that help teams move from out-of-the-box deployments to environments where AI is tuned to their specific threat landscape, detection strategies, and incident patterns.


    3. Reduction of Cognitive Load

    The current model increases analyst cognitive burden. Analysts now juggle multiple AI assistants, each with different interfaces, outputs, and decision-making logic. The next wave should consolidate this into a unified AI partner that understands the full context of the investigation and can confidently recommend actions or escalations.


    ## Implications for Organizations


    For security leaders evaluating or expanding AI in their SOCs, the data suggests several hard truths:


  • Deployment alone is insufficient. Buying and deploying AI tools will not improve outcomes if those tools are isolated and disconnected from your operational workflow.
  • Customization matters significantly. Organizations that invest in tuning AI to their specific environment, threat models, and team dynamics report better results. The 20% of shapers and 15% of builders are outperforming the 65% of takers by a measurable margin.
  • Best practices are missing. The industry has not yet codified how to successfully implement AI in SOCs. Security leaders should prioritize guidance and frameworks over raw feature count.
  • The real work is workflow redesign. Adding AI is not primarily a technology challenge; it's an operational challenge. Organizations need to re-examine how their SOC actually works and where AI can close gaps, rather than simply bolting AI onto existing processes.

    • ## Recommendations


    For SOC leaders:

  • Audit your current AI deployments. Identify where context handoffs are happening and where information is being lost between tools.
  • Resist the temptation to adopt every AI feature. Focus on specific, high-impact areas where integrated AI can genuinely reduce analyst burden or improve detection quality.
  • Invest in customization. Generic, off-the-shelf AI configured with default parameters is delivering the poorest outcomes. Start moving toward customized deployments tuned to your environment.
  • Establish AI maturity frameworks. Work with vendors and internal teams to define what "mature" AI integration looks like in your SOC and create a roadmap to get there.

    • For vendors:

  • Stop treating AI as a feature. Build AI as an operational layer that spans your entire security platform.
  • Provide migration guidance. The "taker" problem won't solve itself. Vendors need to ship documented pathways to help organizations transition toward customized, integrated implementations.
  • Invest in interoperability. AI features that can't share context with adjacent tools are inherently limited. The next wave will require standards and integrations that allow AI across the security stack to communicate.

    • ---


    ## HackWire Analysis


    The AI SOC story is experiencing a critical inflection point. The first wave generated tremendous excitement and marketing momentum but failed to deliver proportional value—not because the technology didn't work, but because it was deployed in isolation, creating a fragmented experience that actually increased analyst cognitive load. The real insight here is that the second wave will be won by vendors and organizations that treat AI not as a bolt-on feature but as an operational foundation that redesigns how SOCs work.


    What's particularly telling is that the survey data shows the "taker" problem is universal—it affects hybrid SOCs, in-house teams, and MSSPs equally. That uniformity suggests this isn't a skill gap or execution gap at individual organizations. It's a product and architecture gap. The vendors shipping point solutions are solving SOC-adjacent problems, not the SOC's actual problem. The SOC's actual problem is context loss at handoffs. A SOC analyst spends most of their time stitching together fragmented information across disconnected tools. An AI system that works within a single tool but can't share context with the next one doesn't fix that. It amplifies it.


    The organizations reporting excellent or good value (29% combined) are likely in the "shaper" and "builder" categories—teams that either customized their deployments or trained models against their own data. That's a small minority, and it shouldn't be. If AI value is locked behind high customization cost and organizational maturity, it becomes a tool that widens the capability gap between large, sophisticated SOCs and everyone else. The industry has an opportunity to democratize this through better product architecture and integrated frameworks—but only if vendors recognize that "more AI features" is the wrong answer to this problem.


    The second wave will belong to whoever builds the unified threat investigation assistant that has full context across the entire SOC workflow and reduces instead of multiplies the number of AI assistants an analyst has to manage. That's not primarily an LLM problem. It's an architecture and integration problem.


    — HackWire Editorial


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