The Future of SOC Is Agentic: AI Redefines Threat Detection and Response
Today we feature Aqsa Taylor, a cybersecurity expert at Exaforce, discussing the integration of agentic AI within Security Operations Centers (SOC). She explains how their platform differentiates itself by using a semantic context layer to move beyond simple alert triaging toward active threat hunting and automated response. We highlight the necessity of AI in defense to match the speed of AI-driven attacks while emphasizing the importance of human judgment and transparency in model decision-making.
Watch&Listen to the full interview here.
Aqsa Taylor
Chief Cybersecurity Evangelist at Exaforce
With hundreds of logos under the “AI SOC” umbrella, how does agentic AI actually differ from traditional automation?
Many traditional tools offer “shallow dashboards” that look pretty but lack depth. Agentic AI, specifically through platforms like Exaforce, builds a “semantic layer” or “knowledge layer”. Instead of just triaging alerts from third-party providers, it ingests configuration, identity, and code security data to understand the specific environment in which an alert originated. This allows the AI to establish a baseline of “good” behavior for that specific organization, making it far more effective at identifying true threats
What are the “four pillars” that define a complete SOC cycle in this new era?
A truly agentic platform must cover triaging, threat hunting, investigation, and response. Most tools only focus on triaging to reduce false positives. The Exaforce Agentic AI goes further by actively hunting for threats with its own detection rules and using automation agents, ExaBots, to pull real-time data, like new Indicators of Compromise (IOCs) from newsfeeds, and running them against your environment automatically.
Is the shift toward AI-driven security just a trend, or is it a necessity?
It is a necessity because AI is a double-edged sword. Attackers are already using AI to scale their efforts exponentially, turning tasks that used to take days into ones that take mere hours. Defenders cannot keep up with that speed and scale using manual scripts or traditional methods alone; they must use AI as a tool to transform their defensive strategies.
There is a common fear that AI will replace human analysts. How does the “judgment zone” concept address this?
AI isn’t meant to replace the human but rather to elevate them. By automating mundane tasks like “stitching context” and gathering data across platforms, AI moves the analyst into the “judgment zone”. In this zone, the analyst isn’t buried in logs – they are making high-level, creative, and risk-based decisions on a shortlist of real, reasoned threats.
How can a security leader tell if a vendor’s “AI SOC” tool is legitimate or just marketing hype?
You have to “go behind the curtains”. Don’t just ask if they use AI; ask how they use it. Key questions include:
What type of data are you relying on for your claims?
How is data cleaned and pre-processed before it reaches the models?
Does the platform offer explainability – can you see the AI’s “thinking” and reasoning?
Does it use historical data (30 to 90 days) to establish baseline behavior?
Finally, looking toward the future, what broader changes do you see coming to the industry?
We will certainly see more women and diverse leaders on the technical side, which is vital for bringing creative problem-solving to the field. For founders and vendors, the focus must shift from “innovation for innovation’s sake” to solving specific customer use cases. If a task can be done without AI, it probably isn’t a true agentic solution. The future belongs to tools that prioritize value and transparency over hype.