AI agents are moving into security operations, where they can help investigate alerts, correlate evidence, prioritize risk, and coordinate response. For industrial organizations, the opportunity is significant. So is the responsibility that comes with giving AI more influence over security decisions.
An action that is relatively contained in IT can have very different consequences in OT. Blocking an account, terminating a connection, or isolating a system may interrupt production, prevent an engineer from restoring a machine, delay critical maintenance, or affect a physical process.
This changes the agentic SOC discussion for OT. Faster investigation has benefits, but industrial organizations also need to determine what evidence AI should use, how much authority it should have, where human judgment remains essential, and how existing OT systems can provide the information needed to make those decisions safely.
Key takeaways
- An agentic SOC uses AI agents to perform multi-step security investigation and response work within defined permissions, approval points, and escalation rules.
- OT needs a different approach from IT because security actions can affect production, maintenance, recovery, and physical safety.
- AI-assisted response should depend on decision-relevant evidence such as identity, access reason, approval, asset context, session activity, and operational outcomes.
- The safest response to suspicious OT activity may involve controlled escalation – adding verification, monitoring, or approval while more evidence is gathered.
- Secure Remote Access platforms can provide valuable OT-specific evidence to existing SOC and SIEM workflows, including around legacy equipment with limited native telemetry.
- Readiness starts with strong security fundamentals, useful operational baselines, clear response playbooks, and defined human-in-the-loop thresholds.
In this article
- What is an Agentic SOC, and how does it differ from a traditional SOC?
- Why Agentic SOC matters for OT now
- Why OT needs a different Agentic SOC model
- What does an Agentic SOC need to understand before it acts?
- From alert to action: designing safer AI-assisted response in OT
- Building the evidence foundation for Agentic SOC in OT
- Preparing your organization for Agentic SOC
- Where Agentic SOC in OT goes next
- What is an Agentic SOC, and how does it differ from a traditional SOC?
What is an agentic SOC, and how does it differ from a traditional SOC?
A Security Operations Center (SOC) is the operating function that brings together people, processes, and technology to monitor, investigate, and respond to cyber threats.
Within that environment, different technologies support different parts of the workflow. A SIEM helps the SOC collect and correlate security information and create alerts, while SOAR technology executes predefined playbooks and coordinates actions across security tools. An Agentic SOC adds AI agents that can work across these layers, reasoning over evidence and carrying out multi-step investigation or response tasks within defined permissions, approval points, and escalation rules.
Component | Role |
SOC | The operating function that brings together people, processes, and technology to monitor, investigate, and respond to cyber threats. |
SIEM | Collects and correlates security data and creates alerts. |
SOAR | Executes defined playbooks and coordinates actions across security tools. |
Agentic SOC | Introduces AI agents that can reason across evidence and perform multi-step investigation or response tasks within defined permissions and authority boundaries. |
The main difference lies in how much reasoning and initiative can happen between the initial signal and the eventual response. A SIEM helps surface what is happening, and SOAR can execute actions that have already been defined in a playbook. In an agentic SOC, AI agents can take on more of the investigation itself – gathering additional evidence, connecting information across systems, prioritizing risk, recommending next steps, and, where permitted, executing parts of the response workflow.
That does not mean an agentic SOC is necessarily fully autonomous. The level of authority given to AI can vary considerably. An agent may be allowed to enrich an alert or open a case automatically while still requiring human approval before restricting access or taking an action with operational consequences.
This distinction becomes particularly important in OT, where the impact of a security decision can extend beyond the digital environment into production, maintenance, recovery, and safety.
Research into agentic security shows how differently that authority is currently being approached on the offensive and defensive sides. In a 2026 survey of published systems, 37 of 42 offensive systems operated at the study’s highest autonomy level, where the system could plan, reason, and execute an end-to-end task with minimal or no human intervention. None of the 34 defensive systems reached that level, and all retained human approval. The figures illustrate the stronger safety, accountability, and operational constraints placed on defensive autonomy.
Why Agentic SOC matters for OT now
The interest in agentic SOC is closely connected to a growing mismatch between the speed of cyber activity and the speed of human-led investigation.
Attackers can increasingly automate reconnaissance, vulnerability discovery, credential use, and other parts of their workflow. Zero Day Clock data also shows the pressure around newly disclosed vulnerabilities: in the first half of 2026, 486 vulnerabilities were newly cataloged as exploited, and 135 were already being exploited when they became public. Zero Day Clock cautions that catalog and publication dates should not be treated as exact timestamps for when exploitation began, but the data still illustrates how little reaction time defenders may have once a vulnerability becomes publicly actionable.
Another indication of the speed gap is the difference between attacker activity and human-led response: a threat actor can move from stolen credentials to full access in around eight minutes, while human triage can take around fourteen minutes to analyze a compromised system and develop remediation recommendations. The precise number will vary by incident and organization.
Manually investigating every signal becomes increasingly difficult as attacks and environments become more complex.
For OT teams, closing the speed gap is only part of the problem. The response also has to account for what is happening on the factory floor.
Why OT needs a different agentic SOC model
Three characteristics make AI-assisted security response particularly challenging in OT.
- First, the data foundation is often more fragmented. Relevant evidence may sit across remote access platforms, network tools, maintenance tickets, engineering workflows, machine data, and local systems at individual plants.
- Second, OT contains many legitimate operational exceptions. A vendor connecting outside normal working hours, an engineer accessing a machine they rarely touch, or an unexpected configuration change can deserve investigation without necessarily indicating malicious activity.
- Third, the consequences of an incorrect response are different. In an IT environment, automated containment might temporarily disable an account or isolate an endpoint. In OT, terminating the wrong session could interrupt production or prevent an engineer from carrying out urgent recovery work. CISA and partner agencies similarly emphasize safety, reliability, human oversight, and operational impact when introducing AI into OT environments.
This creates a different decision problem for an agentic SOC. The system has to assess security risk alongside the operational consequences of acting – or of waiting.
Visibility is already a challenge before AI enters the picture. In the State of Industrial Remote Access 2026 research among 400 industry leaders, three out of four respondents said they had good or full control over OT remote access, while more than half could not fully see what third parties were doing once connected.
AI cannot resolve that uncertainty simply by processing incomplete evidence faster.
What does an Agentic SOC need to understand before it acts?
The missing evidence behind an OT alert
A SOC may already have asset inventories, vulnerability information, authentication events, network anomalies, and endpoint or firewall alerts.
Those sources can show that something unusual occurred. They may still leave important operational questions unanswered.
Why did the person connect? Was the access expected and approved? Which machine was actually being serviced? Which vendor was responsible? What happened during the session? What changed afterward?
Remote activity often leaves evidence behind, but that evidence can be split between different access paths, tickets, audit logs, machine systems, and people with local knowledge. The investigation then becomes an exercise in reconstructing what happened after the fact.
The right data is not all the data
OT environments can generate enormous amounts of information. Sending all of it directly to the SOC would create its own problems.
As Jeff Stahlnecker and Jesper Andersen discussed in our Talk OT to Me episode, SOC teams already struggle with alert volumes. Adding more unfiltered OT data can increase noise, cost, and investigation workload without improving the quality of the decision.
The more useful question is which information changes the interpretation of the event.
For a remote access investigation, decision-relevant evidence might include the user’s identity, the reason for access, the approval status, the asset involved, the actions performed during the session, and selected information about what happened to the machine afterward.
As Jeff summarized it during the podcast, “the right data isn’t all data”. The systems closest to the activity can help identify and contextualize the relevant evidence before it reaches the SOC.
Operational outcomes complete the picture
Consider an engineer who connects remotely and changes a machine configuration.
The session record may show an authorized user performing a legitimate action. Machine data might later show that production recovered, a temperature rose unexpectedly, or a new alarm appeared.
Viewed independently, neither source explains the full event.
Connecting the remote intervention with the relevant operational outcome gives the SOC a better basis for understanding cause, effect, and risk.
The SOC only needs the process data that helps establish whether the remote activity produced the expected outcome.
AI needs an understanding of “normal”
Agentic SOCs also need baselines, although “normal” in OT is highly contextual.
A machine supplier may only access a particular PLC once a year. An engineer may connect outside working hours only when something is already broken. An asset may operate for years without requiring a configuration change.
Useful baselines therefore need to consider the site, asset, engineer or vendor, shift, maintenance window, approval pattern, production state, and typical operational outcome.
Only then can they help distinguish activity that is explainable, activity that requires more evidence, and activity that should trigger escalation.
From alert to action: designing safer AI-assisted response in OT
Consider a remote connection at 2:03 a.m.
Imagine an engineer remotely connecting to a production asset at 2:03 a.m. following an unexpected fault.
The SOC sees an unusual login time, access to an unfamiliar asset, and configuration changes. Based on those signals alone, the activity deserves scrutiny.
Now add more information: the engineer is known; emergency access was requested; the plant manager approved it; access was limited to the affected PLC, and an open maintenance case explains why the work is taking place.
The signals have not disappeared. Their meaning has changed.
The event is still unusual, but the available evidence now provides a plausible operational explanation. That distinction is difficult for an automated system to make when it sees only isolated security telemetry.
Controlled escalation creates more response options
Security response is often described as a choice between allowing activity to continue and blocking it.
OT benefits from a wider range of options.
When an event looks suspicious but the available evidence is incomplete, an AI-assisted workflow could introduce measured friction. Depending on the risk, that might involve
- step-up authentication,
- requiring session recording,
- requesting a second approval,
- bringing another engineer or SOC analyst into the session,
- increasing monitoring,
- narrowing permissions,
- opening a case,
- or notifying the responsible site owner.
Future workflows could also use AI-assisted session summaries or transcripts to provide additional evidence while work is in progress.
The purpose of this friction is to create enough visibility and verification to support the next decision.
Crucially, controlled escalation does not mean waiting fourteen minutes while an attacker continues uninterrupted. Some forms of friction can be applied automatically within seconds. Step-up authentication, a recording requirement, restricted access scope, or an additional verification step can slow suspicious activity immediately while human judgment is brought into the process.
Where should humans remain in the loop?
The appropriate automation threshold depends on the potential operational impact of the action.
AI can begin with low-risk, non-disruptive tasks such as enriching alerts, correlating evidence, opening cases, notifying the right people, or recommending next steps. These activities can accelerate investigation without directly changing the state of a critical asset or interrupting legitimate work.
Human validation becomes more important when a response could affect production, safety, recovery, or access to critical assets. Restricting an engineer’s permissions, terminating a maintenance session, or isolating a production system requires a much higher degree of confidence than gathering additional evidence.
A practical response model can move through observe, enrich, escalate, add friction, restrict, and block, with explicit approval requirements attached to each stage. The greater the potential operational impact, the stronger the need for trusted evidence, predefined playbooks, and human validation.
Building the evidence foundation for Agentic SOC in OT
Why remote access is a valuable source of OT evidence
Remote service sits at an unusually useful point in the OT workflow.
A remote access transaction can connect the vendor, identity, approval, asset, session, activity, and operational outcome associated with a piece of work. Taken together, those elements provide a coherent unit for an analyst – or eventually an AI agent – to investigate.
This also explains why Secure Remote Access can become relevant far beyond connectivity itself.
The progression begins with enabling the connection. Governance adds identity, least privilege, approvals, and auditability. The next step is contextualizing the activity by linking the identity, session, asset, actions, and relevant outcome. That evidence can then enrich investigation, escalation, and future AI-assisted response workflows.
How Secure Remote Access fits into an existing SOC or SIEM
Secure Remote Access does not replace the SOC, SIEM, or SOAR.
Its role is to provide OT-specific evidence that those systems may not otherwise have.
Secomea can integrate with SIEM environments through Syslog and Splunk, while its remote access capabilities provide audit information about who accessed which asset and when. Secomea Prime can also support access approval and session recording, helping organizations retain evidence surrounding remote service activity.
For an existing SOC, that means a remote access alert can arrive with more of the information required to investigate it. Instead of another disconnected event, the SOC can receive evidence about the user, access governance, target asset, and session.
The longer-term opportunity is to supply AI-assisted security workflows with higher-quality OT evidence rather than simply increasing the number of alerts they have to process.
Building context around legacy OT assets
Legacy equipment does not automatically exclude an organization from agentic SOC readiness.
Older PLCs, HMIs, and industrial systems may provide limited native telemetry, but useful evidence can still be built around the interaction with them.
This is where the choice of remote access infrastructure makes a practical difference. Secomea supports both modern industrial systems and older equipment, including serial and USB connectivity. Organizations can therefore apply identity, access governance, audit trails, session controls, and remote access evidence around machines that were never designed to provide modern security telemetry themselves.
That provides a realistic starting point for brownfield environments. Agentic SOC readiness does not have to depend on replacing every legacy asset first.
This allows organizations to improve investigation, escalation, and decision-making now while preparing for AI-assisted response at a pace that reflects the reality of their existing OT environment.
Preparing your organization for Agentic SOC
Start with the security fundamentals
Discussions about AI can easily draw attention toward sophisticated models and autonomous response while more familiar OT security gaps remain unresolved.
Many OT environments still have fundamental security gaps to address. Default passwords need to be changed, MFA implemented, firewall rules maintained, and user accounts and permissions governed consistently. Access that is no longer required needs to be removed.
These controls matter independently of Agentic SOC, and they also improve the evidence AI will eventually rely on. An AI system cannot make a reliable judgment about identity, permissions, or expected behavior when the underlying access model is poorly governed.
The same applies to security by obscurity. Industrial complexity has sometimes made environments difficult for attackers to understand, but AI-assisted reconnaissance can reduce the time required to map unfamiliar systems. Strong fundamentals become more important as that practical barrier erodes.
Agentic SOC readiness requires shared IT/OT ownership
SOC and IT security teams understand security tooling, investigation workflows, incident response, identity, and enterprise governance.
OT and plant operations understand asset criticality, maintenance practices, vendor behavior, production state, and the consequences of disrupting a process.
An effective governance model needs both perspectives.
The SOC may own the security workflow, while OT provides the operational context required to interpret events. Plant operations should help define which actions can safely occur automatically and which require explicit approval. These responsibilities become particularly important when designing escalation rules for critical assets.
Define AI authority before you automate response
Agentic SOC readiness is therefore a governance issue as much as a data issue.
Organizations should decide in advance what an AI agent is allowed to do under different circumstances. It may be permitted to gather more evidence automatically, notify a site owner, request additional authentication, or open a case. Restricting access may require a higher confidence threshold. Blocking or terminating activity on a critical asset may require human approval.
Clear playbooks make those boundaries repeatable.
Without them, an AI system has no operational framework for deciding when unusual activity should be observed, investigated, slowed down, escalated, or stopped. A practical response model can progress from observe → enrich → escalate → add friction → restrict/block, with authority increasing only as risk and operational impact become better understood.
A practical Agentic SOC readiness checklist
Before giving AI-assisted workflows greater authority in OT, industrial organizations should be able to answer the following questions:
- Can we reliably identify the employees, engineers, and third parties accessing OT?
- Can we explain why access was requested, who approved it, and which assets the user was authorized to reach?
- Can we reconstruct what happened during a remote session?
- Do we understand what normal remote access looks like across users, vendors, sites, assets, and maintenance windows?
- Can we connect important remote actions with relevant operational outcomes?
- Have we decided which OT evidence belongs in the SOC or SIEM and which should be filtered or contextualized first?
- Have we defined which patterns should trigger investigation, additional verification, controlled escalation, restriction, or blocking?
- Are human-in-the-loop thresholds explicit for actions that could affect production, recovery, or safety?
- Are SOC, IT security, OT, and plant operations aligned on those response boundaries?
In short: strengthen the fundamentals, capture trustworthy remote access evidence, establish baselines, connect activity with outcomes, and define response authority.
Where Agentic SOC in OT goes next
Several important questions remain open because Agentic SOC itself is still evolving.
One is where AI processing should happen. Industrial organizations may choose cloud-based models for some tasks, local models for others, or hybrid architectures that balance capability, latency, confidentiality, and operational control.
Greater industry collaboration around OT-specific models and datasets may also become important because industrial environments are highly specialized and useful training data can be difficult to obtain. Open-source approaches may play a role, although data ownership, confidentiality, model security, and governance will all need careful consideration.
There is also a broader organizational question around pace. Agentic security technology is changing quickly, while industrial assets and architectures can remain in service for decades. Trying to predict the final technology stack is therefore less useful than improving the foundations that should remain valuable regardless of which platform or model eventually dominates.
That means understanding your data, choosing partners that can work with your existing OT environment, strengthening access governance, and making response authority explicit.
Preparing for Agentic SOC starts before autonomous response
Agentic SOC has the potential to help security teams investigate events, connect evidence, and coordinate response at a scale that manual workflows struggle to sustain.
In OT, its usefulness will depend on how well those capabilities reflect operational reality.
Industrial organizations need reliable evidence about who is accessing their environments, why the access exists, what has been approved, which assets are involved, what happens during the work, and whether the operational outcome is expected. They also need clear rules governing what AI may do with that information.
Much of that preparation can begin today without waiting for fully autonomous security operations. Stronger identity and access governance, better remote access evidence, meaningful baselines, selected operational outcomes, and well-defined response playbooks all improve current security operations while creating a better foundation for future AI-assisted response.
Want to go deeper on Agentic SOC in OT?
Here are three related resources that explore the topic from different angles.
- Read our LinkedIn newsletter, “Why Agentic SOCs in OT need a better context layer,” for a closer look at the operational context AI needs to interpret OT security events more accurately.
- Watch the on-demand webinar, “Agentic SOC in OT: how to design safer AI-assisted response,” for a practical walkthrough of response authority, controlled escalation, remote access evidence, and readiness before automation.
- Listen to the Talk OT to Me episode, “AI Agents vs. OT Security: Can the SOC keep up?”, for a deeper discussion on the speed gap, OT data challenges, legacy environments, and how industrial security teams can prepare for more agentic workflows.
Frequently asked questions (FAQ)
What is an Agentic SOC in OT?
An Agentic SOC in OT is a security operations model in which AI agents help investigate, correlate, prioritize, and potentially respond to cybersecurity events affecting operational technology. Their authority is governed through permissions, playbooks, approval points, and escalation rules designed to account for operational impact.
How is an Agentic SOC different from a SIEM or SOAR?
A SIEM primarily collects and correlates security information and generates alerts. SOAR technology executes defined automation playbooks across security tools. An Agentic SOC adds AI agents capable of reasoning across evidence and carrying out multi-step investigation or response work within defined authority boundaries.
Why does Agentic SOC need a different approach in OT?
OT security actions can affect physical operations. Blocking the wrong user or terminating the wrong connection may interrupt production, delay maintenance, or interfere with recovery. AI-assisted response therefore needs operational evidence and clearly defined human approval thresholds alongside conventional security telemetry.
At what point should an AI-assisted SOC take action automatically in OT?
AI can start by automating low-risk, non-disruptive actions such as enriching alerts, correlating evidence, opening cases, notifying the appropriate teams, or recommending next steps. Human approval should remain part of the process when a response could affect production, safety, recovery, or access to critical assets. The greater the potential operational impact, the stronger the need for trusted evidence, clear response playbooks, and human validation.
How much OT data should be sent to a SOC or SIEM?
The useful objective is decision-relevant evidence rather than maximum data volume. The SOC may need to know who connected, why, whether the access was approved, which asset was involved, what happened during the session, and whether the resulting operational behavior was expected. Filtering and contextualizing data before it reaches the SOC can reduce noise and cost.
Who should own Agentic SOC readiness in an industrial organization?
Agentic SOC readiness should be a shared responsibility across IT security, the SOC, OT, and plant operations. SOC and IT security teams bring expertise in detection, investigation, response, and security governance, while OT and plant teams understand asset criticality, maintenance practices, vendor activity, and production impact. Shared ownership helps ensure that AI-assisted response reflects both cybersecurity risk and operational reality.
Can legacy OT environments prepare for Agentic SOC?
Yes. Legacy assets may produce limited native telemetry, but organizations can still build useful evidence around them through identity, access governance, Secure Remote Access, approval workflows, audit logs, and session information. OT-compatible remote access infrastructure can therefore provide a practical starting point without requiring every legacy asset to be modernized first.
How does Secure Remote Access support Agentic SOC?
Secure Remote Access sits between users, vendors, access policies, sessions, and industrial assets. That makes it a useful source of OT-specific evidence for SOC and SIEM workflows. When access data is connected with relevant session activity and operational outcomes, it can help AI-assisted workflows interpret events more accurately and support safer escalation and response.
How does Secomea fit into an existing SOC or SIEM setup?
Secomea can provide trusted OT remote-access evidence that enriches existing SOC and SIEM workflows. This can include information about who accessed an asset, the applicable access policy or approval, which asset was involved, and what happened during the session.
Secomea does not replace the SOC or SIEM. It adds OT-specific evidence that may be missing from traditional security telemetry, helping analysts move from isolated alerts toward a more complete understanding of the event. For AI-assisted SOC workflows, that context can support more informed investigation, escalation, and response.
