John Brown
Member
Deepwatch has unveiled its NEXA™ ecosystem, an emerging agentic AI platform designed specifically for managed detection and response (MDR), creating a collaborative interface where human analysts and intelligent agents work together in real time to accelerate threat detection and containment.
What NEXA™ Delivers
- NEXA integrates six intelligent agents across security operations - for example, a CTEM Agent for asset exposure insights, a Detection Advisor Agent aligned with MITER ATT&CK, a Ticket Analyzer, Investigative, Narrative and Response agents.
- It unifies detection, investigation and response into a continuous feedback loop so the system evolves as it runs improving coverage and transparency over time.
- Users, whether SOC analysts or business executives, can interact via natural language to explore security posture, ask questions and get board-ready summaries or detailed investigation findings.
- By combining human expertise with agentic AI, Deepwatch claims to reduce low/medium severity alerts by 98 %, accelerate detection by up to 10× and deliver greater clarity into security outcomes.
Why It Matters
- Traditional MDR services focus on automating analyst workflows behind the scenes. NEXA represents a shift: bringing AI into the customer experience, not just operations.
- With cyber threats becoming more autonomous and dynamic, organizations need systems that adapt in real time NEXA's agentic design meets that need.
- For board-level and business stakeholders, clarity of insights and unified intelligence across tools is increasingly important. NEXA aims to deliver that.
- By unifying toolsets and data, the platform potentially reduces analyst fatigue, silos and manual triage, improves operational efficiency and defensive posture.
Considerations & Implementation Notes
- Organizations should assess how NEXA integrates with their existing security stack (SIEM, EDR, vulnerability management, ticketing) and how data flows into the agentic ecosystem.
- Policies and guardrails matter: While autonomous agents provide speed, defined boundaries and governance frameworks are required to ensure safe and audit-ready operations.
- Change-management: Moving from traditional MDR tools to an agentic AI ecosystem requires training analytics, security leadership and business users in new workflows and interfaces.
- Metrics of success should be defined ahead of rollout eg, reduction in mean time to detect/contain, number of actionable insights generated, reduction of false positives and improved coverage.
- Scalability and future-readiness: As cyber-risk evolves, confirming that the ecosystem can incorporate new agents, support new telemetry sources and maintain transparency is critical.
Read related news - https://soc-news.com/zenity-unveils-runtime-protection-for-ai-agents/