Well-equipped security teams can spot threats faster than ever, but their response times are no match for AI.
In today’s technical environments, cloud, SaaS, endpoints, and identities are connected and running all at once, creating more alerts, data, and operational noise than humans can handle. At the same time, AI is empowering attackers to infiltrate environments in minutes, while analysts are heads down validating alerts, gathering context, and deciding what to do next.
This delay between discovery and response is a soft spot in modern MDR. The teams overcoming it are applying agentic AI.
Agentic AI ≠ SOAR
Over the years, enterprise teams have invested heavily in security orchestration, automation, and response (SOAR) platforms to automate work. Their goal was the same as it is today. That is, to eliminate the need to do repetitive tasks, reduce analyst workloads, and respond faster to attacks or breaches.
And they did.
But automation can be a deceiving concept. While they still run on their own, typical automation always follows predefined logic. A person has to script the workflow in advance, including what gets escalated, what gets contained, and what happens when anything changes mid-incident. It worked for predictable tasks, but modern cyberthreats are rarely predictable.
AI agents on the other hand, can monitor a much broader attack surface and autonomously decide whether to to contain activity, gather more evidence, or even bring in a human when their judgement is needed. And it all happens in seconds. One moment the system may isolate an endpoint. Minutes later it escalates identity, activity, correlate related signals across cloud, or holds containment until additional evidence is confirmed. And it’s constantly adjusting priorities during its investigation.
But let’s pause here with an important caveat. Agentic systems are only as effective as the data they are given access to. Without that visibility, even AI agents won’t be able to make reliable decisions.
SOC teams work under constant pressure. Analysts are expected to investigate more alerts, move faster, and manage attacks, but many also deal with industry-wide staffing shortages combined with their own alert fatigue.
In the past there, companies could more easily add headcount and extended coverage hours, a move that worked well when investigation volume was lower and a slower response was still adequate. But that’s changed. With the mass application of AI tools, teams are now expected to validate, investigate, and contain threats almost immediately.
The MDR and managed service providers adapting fastest are willingly moving toward a new reality: agentic AI analysis and decision-making + human judgement when it’s needed. AI handles triage, enrichment, and response, while people focus on investigation and complex escalation decisions.
If you’re hiring an MSP or MDR provider, you need to understand more than their staffing models, escalation paths, certifications, and response SLAs, as those metrics no longer tell the complete story.
So, what should you be asking? Here are a few great questions to start with:
MSPs should be able to answer these questions clearly. Fast response is not enough if no one can explain the decision process afterward. If an AI agent isolates a production workload, disables an executive account, or blocks critical access, leadership (especially those in highly regulated industries) will demand to know why the action was taken, what evidence triggered it, and who approved it.
NexusTek helps organizations modernize cybersecurity operations with MDR services designed for today’s complex IT ecosystems and lightning-fast attack patterns. To learn more about NexusTek’s approach to MDR and operational resilience strategies, visit www.nexustek.com/solutions/managed-detection-response.