Topic starter
24/10/2026 8:49 pm
TL;DR: Generative AI is accelerating business processes while compressing the time available to detect, respond and recover, creating fragility across interdependent workflows, third parties and endpoints, according to Absolute Security. The practical issue is no longer stopping every incident, but maintaining operational continuity when failures cascade faster than traditional controls can absorb.
Editorial analysis by NHI Mgmt Group, based on content published by Absolute Security: “AI Isn't Just Changing the Enterprise. It's Quietly Dismantling It.”.
Key questions
Q: What breaks when AI-driven SOC workflows do not keep humans in control of response?
A: Without human control, agentic SOC workflows can turn speed into operational risk.
Q: Why do third-party dependencies increase resilience risk in AI-enabled enterprises?
A: Because a single business process can now depend on external providers, cloud services and AI-supported decisions at the same time.
Q: How do security teams know if resilience testing is actually working?
A: Look for evidence that testing is recurring, mapped to critical controls, and tied to remediation outcomes.
Practitioner guidance
- Map cascade paths across critical workflows Identify where endpoint, cloud, third-party and AI-driven processes intersect so you can see how one failure can spread into a business event.
- Move containment closer to execution time Reduce dependence on slow manual review loops by enforcing faster automated escalation, isolation and recovery triggers in high-risk processes.
- Protect control continuity below the OS Prioritise recovery and enforcement mechanisms that remain available even when local software agents or endpoints are tampered with or removed.
Bottom line: AI is reducing the time available for detection, containment and recovery, which turns speed into a governance issue as much as an operational one.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →