New York, USA, February 17th, 2026, CyberNewswire
Mate Security, an AI-driven security operations company, believes the key to reliable AI lies not in faster algorithms but in smarter data structures. The company has introduced the Security Context Graph, a foundational architecture designed to give AI SOC agents the contextual awareness that human analysts naturally apply when investigating threats.
According to the team, security operations centers are under pressure like never before. Rising alert volumes, expanding attack surfaces, and staffing shortages have made it increasingly difficult for analysts to respond quickly and consistently. AI promises relief, but early deployments have often left CISOs frustrated with opaque reasoning and inconsistent outcomes.
The announcement arrives as organizations increasingly experiment with agentic AI, systems capable of performing investigative tasks and making recommendations autonomously. While such tools can process alerts at high speed, many fail to replicate the nuanced reasoning required for confident security decision-making.
“We are witnessing the AI SOC revolution as we speak,” said Asaf Weiner, Co-Founder and CEO of Mate Security. “AI is slashing alert queues, increasing focus, and speeding up SOC work like never before. Overloaded Tier-1 analysts are being elevated to AI engineers. They are happier!”
Yet skepticism persists. “When I meet a CISO for the first time, I can feel the mistrust,” Weiner said. “They have piloted AI in their SOC and were burned with a bad experience: agents taking months to learn, confidently generating wrong verdicts, and requiring more ‘babysitting’ than the SOAR they were meant to replace.”
Structuring Knowledge for Machine Reasoning
Traditional SOC workflows rely on logs, alerts, and documentation optimized for human analysts. While effective for people, this format often leaves AI agents without the contextual “why” that connects disparate signals and informs accurate decisions.
Mate Security’s Security Context Graph addresses this gap by capturing the operational reasoning analysts apply during investigations. Instead of treating decisions as static outputs or rule sets, the graph transforms security data into contextual memory, or relationships among policies, ownership, investigations, and organizational realities, that AI can traverse and interpret.
“AI agents are fed data structured for humans,” said Weiner. “SOC analysts work with tables, logs, and documents… they rely on their experience and common sense to connect the dots. But AI cannot do that. AI agents need more than the ‘what’, they need the ‘why’: the operational context. This is why we have built the Security Context Graph, the underlying foundation for our agentic AI platform.”
Measurable Improvements Across Four Dimensions
Mate Security reports that AI agents powered by the Security Context Graph are already delivering tangible operational gains. The improvements span four critical areas:
- Accuracy: Agents “get it right” more often by reasoning through context rather than AI using data created for humans.
- Consistency: A single source of truth reduces conflicting verdicts, ensuring predictable outcomes.
- Transparency: AI can explain its reasoning in plain language and highlight uncertainty when additional data is needed.
- Adaptability: The graph continuously updates with every investigation, policy change, and ownership shift, keeping decisions relevant in real time.
“The Security Context Graph is a living and breathing structure,” said Weiner. “It is dynamically rebuilding and optimizing with every investigation, every ownership change, every policy change, so decisions are made according to what’s relevant right now.”
Building Trust Before Deployment
Mate Security emphasizes a data-first approach: the Context Graph was built before the release of its AI agents and has powered enterprise SOCs from day one.
“Agents are only as effective as the data structure on which they are built,” Weiner said. “This is the only way for AI to earn trust.”
By embedding human-like reasoning into a continuously evolving knowledge graph, Mate Security aims to bridge the trust gap that has limited AI adoption in security operations. The architecture not only accelerates investigations but also provides precise, consistent, transparent, and adaptable decision-making that analysts and leadership teams can rely on.
Institutional Memory as a Security Advantage
As SOCs contend with growing complexity and rising threats, the challenge is no longer simply automating investigations; it’s enabling AI to synthesize data from numerous sources and formats to build context as experienced analysts would. Mate Security’s Security Context Graph demonstrates that operational wisdom, structured for machine reasoning, may be the missing link in delivering trustworthy AI at scale.
For organizations navigating constant personnel changes and escalating threat volumes, the future of AI-driven SOCs may depend on retaining and operationalizing organizational knowledge as a persistent security control. As analysts transition roles or leave organizations, their investigative patterns, decisions, and contextual understanding remain embedded within the Security Context Graph, ensuring continuity, consistency, and resilience where context is as critical as computation.
Contact
Tech Analyst
Jake Smiths
TVC Analytics
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