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From Concept to Containment: What an Agentic SOC Looks Like in Practice

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Traditional MDR is good at telling you something happened. The harder question is what happens next.

When an alert arrives at 2:00 a.m. on a Saturday, does your provider begin investigating immediately? Can it assemble the right identity, endpoint, cloud, and business context without waiting for an analyst to pick up the case? Can it decide what to do within policies you have already approved, take the appropriate action, and show you why?

Those questions were at the center of our recent webinar, “From Concept to Containment: The Agentic SOC in Practice,” hosted by Vijay Viswanathan with Ontinue Chief Security Officer Craig Jones. Rather than rely on abstract claims about AI, they walked through three realistic incidents and compared how traditional or AI-assisted MDR would handle them with how an Agentic SOC operates.

The difference is not simply better alert enrichment. It is a different operating model, built to scale security decision-making through agentic AI and automation under human governance.

Incident 1: A low-severity alert that cannot afford to wait

The first scenario begins with an unusual authentication to a privileged account. The credentials and MFA are valid, but the device is unmanaged and has never been seen before. It is the kind of alert that might initially appear low severity, even though the underlying risk could be significant.

Craig’s expectation was direct: investigate quickly, build a timeline, examine the surrounding context, and, if the evidence supports credential theft with high confidence, take a pre-approved response. As he put it in the webinar, “I want them to deal with this for me.”

In a traditional MDR model, the alert may wait in a queue until an analyst reviews it. The analyst then pivots across tools, assembles context, and escalates the final decision to another human. Each handoff creates another waiting state.

An Agentic SOC starts the investigation as soon as the detection fires. It assembles environmental context at machine speed, evaluates benign and malicious explanations, and checks the available response against granular customer authorizations. If the evidence crosses the required threshold and the response is pre-approved, it can secure the account and revoke sign-in sessions in near real time.

The takeaway: severity labels should not dictate whether investigation begins. The real test is whether your provider can investigate, decide, and respond before a seemingly small signal becomes a larger incident.

Incident 2: Knowing when not to escalate

The second scenario combines several potentially suspicious signals: sensitive resources are queried and new administrative tools are accessed late at night. This pattern can indicate malicious discovery activity, but it can also reflect normal administrative or audit work.

This is where “more AI” is not enough. AI-assisted MDR may enrich an alert, correlate signals, and summarize what happened. But if every ambiguous case still lands with the customer, the core bottleneck remains unchanged.

An Agentic SOC uses the organization’s history and context to test hypotheses. Does the query pattern match an approved audit job? Is the account acting within a known maintenance window? Is there related suspicious identity or device activity? When the evidence rules out malicious explanations, the system can close the incident as benign within policy and record the reasoning.

The takeaway: deciding not to act is still a decision. A mature service should be able to make that decision transparently, reduce unnecessary escalations, and leave the customer with nothing to do when the evidence supports a benign outcome.

Incident 3: Autonomy with boundaries

The third scenario raises the stakes. An authentication to a second system and privilege-related activity suggest attempted lateral movement. Immediate containment matters, but one of the available actions could affect a critical business asset.

The right answer is not unrestricted autonomy. It is governed autonomy. The Agentic SOC should execute reversible, account-scoped actions that have already been authorized, while escalating the exceptional action that requires business-impact approval. The escalation should arrive with the attack path, supporting evidence, confidence, policy applied, and a clear recommendation.

Craig highlighted the foundation of trust: the customer must control where the system is autonomous, see how it performs, and expand authority only as confidence grows.

That balance matters. Autonomy without governance creates risk. Governance without autonomy creates delay. The operating model has to deliver both speed and control.Traditional or AI-assisted MDR vs. an Agentic SOC

AreaTraditional / AI-assisted MDRAgentic SOC
InvestigationWaits for an analyst to initiate and pivot across toolsBegins on detection and assembles context without a handoff
DecisionProvides enrichment, summaries, or recommendationsTests hypotheses and decides within explicit policy boundaries
ResponseWaits for a human to act on most or all recommendationsExecutes approved actions and escalates only exceptions
TransparencyMay provide an incident summary after escalationRecords evidence, confidence, decision, and policy applied
ImprovementCloses the incident and moves onUses outcomes to refine hardening guidance and future decision boundaries

Containment is the priority, not the finish line

A contained incident is a good outcome, but it should also improve the security program. After response actions are complete, the provider should help explain why the incident happened, recommend hardening and prevention measures, and review whether decision boundaries should change in the future.

That creates a continuous improvement loop. The customer can start with narrow authority, review decision quality and exceptions, and expand autonomy as trust accumulates. Human expertise remains essential, but it is focused on the decisions where judgment and business context matter most, rather than every routine investigation and response step.

Five questions to ask when evaluating an MDR provider

  1. Can they investigate without waiting for a human to initiate every step?
  2. Can they connect activity using context that is specific to our environment?
  3. Can they make decisions within policies we define?
  4. Can they take initial response actions without another operational handoff?
  5. Can they explain their decisions and continuously improve them?

These questions move the evaluation beyond detection coverage and response-time promises. They reveal whether a provider can operate at the speed of modern attacks while preserving customer control, transparency, and accountability.

Make the evaluation specific to your priorities

No two MDR buying processes are identical. One organization may prioritize deep Microsoft security integration. Another may care most about response authority, auditability, global coverage, communication, or the path to continuous posture improvement. Your evaluation should reflect the outcomes, constraints, and tradeoffs that matter in your environment.

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Ask what your MDR can decide and do, not just what it can detect.

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