Security Automation Has Hit an Inflection Point
Traditional rule-centric, SOAR-based security automation has hit a wall – actually hitting it some years ago. When the first SOAR solutions came to market more than 10 years ago, they swept in to address the problem of SIEMs and other siloed security detection systems generating more alerts then could be handled manually. They did so with the hope that they would improve incident handling efficiency and efficacy. And they did. While they moved the threat response ball down the field, consistent “scoring” became rarer over time. The technology and threat environment became more challenging faster than the automation tools and SOC teams could keep up. Enter AI, and the security automation renaissance is now upon us.
Since the publication of the KuppingerCole SOAR Leadership Compass in Q4 2024, it has become clear that the market has reached a true inflection point. AI-driven analytics and agentic AI approaches are no longer experimental add-ons; they are starting to reshape how detection and response work is performed. As we look ahead to the publication of the Emerging AI SOC Leadership Compass later this spring, one conclusion is clear, the AI-enabled SOC is beginning to take concrete form.
Why the Status Quo Is No Longer Viable
The drivers behind this shift are neither subtle nor new. SOC teams remain overwhelmed by alert volumes, false positives, manual investigations, fragmented tooling, and chronic staffing shortages. These challenges have persisted despite years of tooling investment and process refinement. The uncomfortable truth is that traditional, human-centric operating models simply do not scale. AI-based automation has therefore moved from being a ‘nice to have’ efficiency enhancer to a strategic necessity for maintaining acceptable levels of security monitoring. And every vendor in this space has gotten this same message.
From SOAR to AI-Enabled Augmented Security Operations
At the same time, it is essential to be clear-eyed about what AI can and cannot deliver. The idea of a fully autonomous, lights-off SOC remains unrealistic and undesirable. Security operations demand accountability, contextual awareness, and judgment that cannot be fully automated. Partially challenged by its inherent non-determinism, AI-based systems need to earn the trust of security analysts before increased autonomy can be earned. But this would be true of any new automation capability.
The more credible AI SOC vision is one of augmentation: AI-enabled capabilities that remove low-value, repetitive work from analysts while accelerating investigations and improving consistency. Early adopters are already seeing tangible benefits at Levels 1 and 2 of the SOC and within similar roles at MDR providers.
Platform Vendors vs. Specialists: Old Debates, New Stakes
Vendor strategies in security automation are also crystallizing. Established security providers now treat automation as a mandatory component of their broader portfolios across SIEM, EDR/XDR, cloud security, and ITSM-centric platforms. In practice, no vendor can credibly claim to be a security platform without integrated automation. However, platform approaches inevitably prioritize deep native integration within their platforms over equivalent support for competitive third-party tools, keeping the suite-versus-best-of-breed debate very much alive.
The Case for Independent Security Automation Providers
In parallel a new generation of standalone security automation specialist vendors are gaining momentum. These vendors are not trying to be security platforms; they are focused on being excellent at automation across heterogeneous environments. Their aggressive adoption of AI and agentic capabilities is often faster and more experimental than that of larger incumbents. Buyers must decide whether this pace of innovation outweighs the operational cost of adding another vendor to their security portfolios.
MDR Providers and the Economics of Automation
Managed Detection and Response (MDR) providers represent a third, and often underestimated, force in the evolution of the AI SOC. Given the cost and complexity of running a 24×7 internal SOC, most enterprises rely on MDR services to some degree. And automation is central to MDR business models: providers cannot scale or remain profitable without minimizing their own analysts’ toil. Not surprisingly, many MDR providers are both heavy users and increasingly suppliers of advanced security automation capabilities to their customers. The arrival of AI SOC tools could also change the calculus of using MDR providers at all as some outsourced services could be replaced by AI agents run within the enterprise. Enterprise customers win either way!
How AI Agents Are Actually Being Adopted
KuppingerCole research shows that security practitioners are cautious but constructive when it comes to AI agents in the SOC. Adoption is strongest in alert triage, enrichment, and investigative support, where agents can automatically assemble context, correlate signals across tools, and produce concise summaries. These use cases directly address alert fatigue and analyst burnout. Trust, however, is conditional. Analysts consistently demand transparency, evidence-based reasoning, and the ability to validate agent outputs before acting on their conclusions and recommendations.
Autonomous Responses Requires Trust - Trust Takes Time
Sentiment shifts markedly when discussion turns to autonomous responses. While there is interest in agents that can take on more responsibilities, most organizations insist on human-in-the-loop controls for actions with significant blast radius. The risk of unintended disruption, such as locking out critical users or isolating essential infrastructure, remains too high. As a result, progress toward increased response autonomy is likely to be evolutionary rather than revolutionary.
The Emerging Shape of the AI SOC
The emerging consensus is pragmatic. AI agents are not replacing SOC analysts; they are becoming investigation copilots and junior teammates. Rule-based workflows are also not disappearing and will coexist with AI-driven approaches for the foreseeable future. Rules are deterministic and cheaper than non-deterministic AI techniques. Both have their pluses and minuses. The AI SOC, at least in the near to mid-term, is best understood as a human-led, AI-accelerated operating model. This balance between innovation and operational realism is what will ultimately define the success of this renaissance of security automation.
KuppingerCole Analysts will take the next major step in illuminating this renaissance in the upcoming Leadership Compass – The Emerging AI SOC!
