Security operations centers (SOCs) have long been defined by an impossible mismatch. They sit at the intersection of massive data flow and urgent decision-making, yet the more telemetry organizations collect, the harder it becomes to translate that information into confident, consistent action. Over the past several years, artificial intelligence has been positioned as the solution to alert fatigue and investigation backlogs. But early SOC AI deployments have exposed a deeper structural problem: speed without context rarely builds trust.
Mate Security’s announcement arrives as security leaders continue experimenting with AI to augment SOC teams while remaining wary of opaque decision-making and unreliable automation. According to the company, many of the issues that surfaced in early deployments across the industry stem not from AI limitations but from fragmented data structures designed for humans, not AI, to consume.
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!
Despite these operational gains, many CISOs remain hesitant to fully rely on autonomous or semi-autonomous AI-driven workflows. Weiner says the skepticism is often shaped by the first wave of AI SOC solutions that emerged to solve the SOC challenge but struggled to deliver consistent results.
“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.”
Reimagining SOC Knowledge for Machine Reasoning
Traditional SOC environments organize data in ways that prioritize human consumption. Logs, alerts, documentation and playbooks are optimized for analysts who rely heavily on experience and intuition to identify patterns and contextual relationships. While effective for people, this structure often leaves AI systems attempting to reason across disconnected or incomplete information sets.
Mate Security’s Security Context Graph is designed to address this gap by transforming how operational knowledge is captured and applied. Instead of relying solely on static rules or individual investigation outputs, the architecture builds what the company describes as contextual memory, capturing the reasoning, dependencies, and logic, just like analysts do in their heads.
“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.”
The graph operates as a continuously updated representation of the organization’s institutional knowledge, reflecting policy changes, investigation outcomes, ownership structures, and organizational nuances. By allowing AI agents to traverse these interconnected relationships, Mate Security aims to improve decision-making accuracy while maintaining explainability.
“This is why we have built the Security Context Graph,” Weiner said. “The underlying foundation for our agentic AI platform.”
Closing the Trust Gap Between CISOs and AI
Trust has emerged as one of the most persistent barriers to AI adoption within security operations. Mate Security testifies organizations deploying AI agents built on the Security Context Graph are already seeing substantial improvements in measurable security KPIs, including mean time to respond and time spent on benign alerts and false positives, as well as substantial customer-reported improvements in accuracy, consistency, transparency, and adaptability.
By reasoning through contextual relationships rather than relying exclusively on predefined rules, agents can deliver verdicts that align more tightly with organizational knowledge. The graph also functions as a unified source of truth, reducing inconsistencies that frequently arise when multiple data sources conflict. Additionally, the architecture enables agents to explain conclusions in natural language while acknowledging uncertainty when additional data is required.
One of the major challenges of security operations centers is turnover. Experienced SOC analysts retain “tribal knowledge,” memories and pieces of context that are not documented but serve them in decision-making. When these analysts leave, the knowledge leaves with them. One of the biggest advantages of a Security Context Graph structure is that it preserves institutional memory. It is not affected by analyst turnover; it functions like an experienced mentor, transferring knowledge and experience to less-experienced team members.
“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.”
Notably, Mate Security built the graph architecture before releasing its first AI agent, adopting what it describes as a data-first development model.
“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.”
The company says the Security Context Graph has powered its AI SOC agents since its earliest enterprise deployments and is now being formally introduced to the market as the core architecture behind its platform.
When Institutional Memory Becomes a Security Control
As SOC teams confront expanding attack surfaces, staff shortages, and escalating alert volumes, the industry is increasingly recognizing that automation alone cannot replicate analyst judgment. Instead, the next phase of AI in security may hinge on translating human expertise into scalable, continuously evolving knowledge frameworks.
Mate Security’s approach suggests that the future of AI-powered SOC operations may depend less on replacing analysts and more on preserving their operational wisdom, turning experience into infrastructure capable of guiding machines toward reliable, transparent decision-making at scale.
This story was distributed as a release by Jon Stojan under HackerNoon’s Business Blogging Program.