Talking about “from cybersecurity” to the singular no longer makes sense. The technological landscape has been fragmented, and each specific need calls for a targeted approach: behavioral models, statistical analysis, correlation graphs, vision engines, or generative LLM. We offer an overview of the tools that cyberfensors are now mobilizing.
Behavioral AI: Pillar of abnormal detection
It is the first brick deployed historically in safety tools. By analyzing the behavior of users, terminals or network flows, these models make it possible to identify subtle deviations: unusual connections, access to sensitive resources, volumes of excessive outgoing data.
Used in solutions Ueba (User and Entity Behavior Analytics), this AI is particularly precious for detecting exfiltration, lateral movements or internal compromise. It is generally based on supervised or semi-sub-supervised machine. His main asset: Contextual detection which is not based solely on known signatures.
Statistical models: fast, stable, effective
Less “intelligent” than Deep Learning models, statistical algorithms retain their place in SOC for simple reasons: execution speed, low consumption of resources, native explanability. They are used for alert scoring, log normalization or abnormal frequency detection.
Often combined with rules, they serve as first sorting linefiltering the volume of events and prioritizing weak signals. Their stability over time is an advantage in critical environments where predictibility is essential.
AI on graphs: to orchestrate investigations
Graph systems allow model the relationships between events, entities and resources : Who talks to what, where, when. This approach structures the analysis in an SOC, especially for the correlation of alerts and the investigation of attack chains.
AI are now trained to navigate these graphs to guide analysts, asking the right questions or offering attack assumptions. They allow outstanding And to explore complex scenarios. Their strength lies in their ability to generate reasoning closer to humansbut with the speed of the machine.
Vision models: identify attacks in the interface
Little mentioned, “visual” AI models gain ground. At Microsoft, for example, a model embedded in Edge detects the fraudulent pages encouraging the user to call for false technical support (Scareware). It is not based on the URL, but on the visual interface recognition.
These AIs are precious to analyze files, interfaces, or suspect screenshots. Their local execution guarantees confidentiality and speed. They open the way to a Cybersecurity less dependent on detection based on network or logs.
LLM (Large Language Models): potential, not without limits
They fascinate, but remain under surveillance. Language models like GPT, Claude or Gemini can Summary of alerts, interpret unstructured logs, read a suspicious email or generate remediation suggestions. Used as co -pilotes, they improve the effectiveness of analysts.
But their limits are well known: hallucinations, inconsistencies, latencyand above all a probabilistic logic poorly suited to critical environments. Their use is still confined to tasks with low decision -making impact, or very supervised environments. The future goes through LLM specialized, embedded or orchestratedat the service of more stable modules.
LOOP feedback: the human link
Whatever the IA tool deployed, the return of human experience remains essential. The best architectures incorporate a supervision : Each decision made by AI must be able to be audited, corrected, commented. This makes it possible to cause models, strengthen their relevance, and maintain a logic of trust.
“These are the specialized, well -governed AI, who today bring value”. In the best SoCs, we see emerging from Networks of autonomous agentseach dedicated to a task, controlled by humans who keep the hand.
Not an AI, but an Arsenal of AI
AI is not a miracle product, it is a Complex toolboxto assemble with method. Each SOC layer – detection, correlation, investigation, remediation – calls for a different approach. It is not a question of automating, but to increase human intelligence with discernment.
The challenge is no longer to adopt AI, but to know What AI, for what use, with what supervision. Advanced defenders do not look for a unique co -pilot, but a Complementary intelligence ecosystemin the service of distributed, reactive and sustainable cybersecurity.
