There is a useful test to apply to any technology category that arrives in a wave of vendor messaging: ask the practitioners who use it day-to-day whether their work has actually changed.
Eighteen months ago, when “AI in cybersecurity” was the dominant marketing theme, that test mostly returned negative results. Practitioners reported new dashboards, new branding on existing detection logic, and a higher density of “AI-powered” badges on vendor collateral. Day-to-day work had not changed materially.
The test today returns different answers. SOC analysts in environments that have deployed current-generation AI capability — particularly the large language model-based investigation assistants and the AI-driven correlation engines — describe their work as meaningfully different than 18 months ago. The change is not what the marketing predicted, but it is real.
This is worth understanding in some detail, because the second-order effects on SOC operations are larger than the first-order capability shift.
What current-generation AI actually does in detection-and-response
The honest answer is: it accelerates investigation, not detection.
Detection — the identification of malicious activity in noise — has been substantially machine-learning-based for years. Anomaly detection, behavioural baselining, and supervised classification of known-bad indicators have all been operational since well before the current AI moment. The current-generation models have improved this layer incrementally, not transformatively. Where they have made transformative changes is in the layer that comes afterdetection: investigation.
When an alert fires, an analyst’s job historically has been to assemble context. Pull the related telemetry. Cross-reference threat intelligence. Check the user’s recent activity. Validate the asset’s role. Determine whether the activity matches known TTPs. Decide on an action. This work — investigation — has consumed the majority of SOC analyst time for the last decade.
Current-generation AI capability does this assembly automatically. The analyst opens an alert and finds it pre-populated with the context that previously required manual pivot across five or six systems. The AI has read the alert, queried the relevant data sources, summarised the user’s recent activity, looked up the indicators in threat intelligence, and proposed a likely classification with reasoning. The analyst’s job becomes verification and decision rather than assembly.
In environments where this is deployed and properly configured, mean time to investigate has dropped 50-70% in the engagements where we have measured both before and after. The improvement is material and reproducible.
What it does not do
It does not replace analysts. The capability is consistently described, by practitioners who use it well, as an “investigation accelerator” rather than an “investigation replacement”. The decisions that matter — escalation, containment, communication with affected business units — remain the analyst’s. The AI supports the decision; it does not make it.
It does not eliminate alert fatigue. The volume of alerts entering the SOC is unchanged. What changes is the cognitive cost of working through them. Analysts who were closing 80 alerts per shift can now meaningfully investigate 80 alerts per shift, where previously they were triaging without time to investigate. This is a quality improvement, not a quantity one.
It does not detect attacks the existing detection logic does not see. Current-generation AI in the SOC operates on the telemetry the platform already collects. If the telemetry has gaps — particularly in identity, network, or cloud layers — the AI cannot fill them. The procurement of AI capability is not a substitute for closing telemetry gaps.
It does not work without continued human curation. The models are trained on enterprise environments in general, not your environment specifically. Tuning for your asset criticality, your user behaviour patterns, and your threat model takes ongoing analyst attention. The platforms that perform best are not the ones with the most advanced models; they are the ones with the strongest feedback loops between analyst decisions and model behaviour.
What this changes in SOC operations
The interesting consequences of this capability are organisational rather than technical.
Tier-one analysts can do tier-two work. When investigation context is pre-assembled, the skill required to make a defensible decision drops. Junior analysts make better decisions earlier in their careers. The tier-one-to-tier-two boundary, which has historically been the bottleneck in SOC career progression, becomes less rigid.
Threat hunting becomes more accessible. Hunting that previously required senior analyst time to formulate and execute hypotheses becomes accessible to mid-level analysts. The hypotheses themselves can be AI-suggested based on current intelligence; the execution is faster because the data assembly is automated.
Documentation improves automatically. AI-generated investigation summaries become part of the audit trail. Analysts who previously closed tickets with terse comments now have rich context attached to each decision, automatically. This is meaningful for regulatory environments where evidentiary quality matters.
The analyst-to-coverage ratio improves. Most SOCs operate at higher analyst-to-endpoint ratios than they would prefer. AI capability does not eliminate the need for analysts, but it does meaningfully increase the volume of telemetry a single analyst can handle responsibly. This is the practical answer to the analyst hiring shortage: existing analysts handling more, not fewer analysts being needed.
The new failure modes
There are also failure modes that are specific to AI-augmented SOCs and worth understanding.
Over-reliance on AI confidence scores. The AI proposes a likely classification with stated confidence. Analysts, under time pressure, learn to trust the high-confidence classifications and skip the verification. When the AI is wrong on a high-confidence classification — which happens — the missed detection is more consequential than it would have been in a manual workflow.
Skill atrophy. Analysts who never assembled investigation context manually do not develop the intuition for which signals matter. When the AI is unavailable (deployment issues, edge-case alerts, novel attacks the model has not seen), these analysts perform less well than analysts who developed the manual skill first.
Vendor lock-in at a deeper level. The AI capability depends on the vendor’s data, models, and continuous updates. Switching detection-and-response platforms now means switching AI investigation capability — a more disruptive change than swapping detection rules between platforms. Procurement decisions made with this in mind look different than procurement decisions made before this capability mattered.
These failure modes are manageable. They are not, however, automatic to manage. The SOCs that handle the AI transition well are explicitly attentive to them; the SOCs that do not, accumulate the risk over time.
Honest expectations for 2026
The capability is real. The improvement to SOC operations is measurable. The vendor messaging is, mostly, no longer ahead of the substance.
What remains true is that AI capability is one component of an effective detection-and-response architecture, not a replacement for the rest of it. The fundamentals — telemetry quality, detection logic, response playbooks, analyst skill, organisational decision authority — still matter. AI accelerates work in environments where these fundamentals are strong. It does not compensate for environments where they are weak.
The pragmatic question for 2026 is not “should we adopt AI in our SOC”. It is “have we built the operational foundation that AI can meaningfully accelerate”. For most organisations, that question has a more useful answer than the headline procurement decision.
Mellivor advises enterprise teams on detection-and-response architecture, including the integration of AI-augmented investigation capability with platforms like Cybereason XDR and Lima Charlie. To discuss your SOC operating model, book a consultation.
