The Tool Desk
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What is being compared?
“Traditional security operations” is not a single product. It is the operating system of a security program: people, procedures, sensors, logs, asset inventories, monitoring, investigation, incident response, maintenance windows, and the authority to make or approve changes. A conventional security operations center (SOC) may use signatures, rules, statistical thresholds, analyst-written detections, ticketing workflows, threat intelligence, and manual investigation. Its effectiveness depends on the quality of its telemetry, the context available to analysts, and the organization’s ability to act safely.
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“AI-powered threat detection” is a capability embedded in that operating system. It can include machine-learning classifiers, behavioral analytics, anomaly detection, large-language-model (LLM) assistants, or AI agents that summarize events or recommend actions. Statistical modeling and logic-based automation can raise many of the same assurance questions even when a vendor does not call them AI. The label matters less than the function, the data used, and the authority granted to the system.
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These approaches are therefore not mutually exclusive. A mature deployment usually combines deterministic controls and human expertise with carefully bounded AI functions. The meaningful comparison is whether the combined operation improves coverage and decision quality while preserving safe, resilient OT processes.
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Where AI may help a critical-infrastructure SOC
The U.S. Department of Energy’s Cybersecurity, Energy Security, Emergency Response (CESER) AI-FORTS program identifies several areas for development and deployment. Those priorities show where AI may be useful; they are not evidence that every deployment has delivered a measured benefit.
Threat detection and anomaly detection
AI can examine relationships among network flows, process values, authentication events, commands, and equipment states that are difficult to express as individual rules. A model might flag a combination of unusual engineering access, a change in a controller’s communication pattern, and a process value moving outside its normal relationship to neighboring equipment. The useful output is not simply a high anomaly score. It is a prioritized, interpretable lead that an analyst and an OT operator can investigate.
Models also have to distinguish a genuine change in operating conditions from a cyber event. A planned outage, seasonal load shift, commissioning activity, or maintenance procedure can look anomalous. Without work-order and process context, more sensitive detection can create more investigation rather than better security.
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AI assistants can search large volumes of historical telemetry for patterns associated with a hypothesis, generate candidate queries, and group related events. This can reduce the time needed to form an initial view of an incident. An analyst still needs to validate the query, understand gaps in the data, and decide whether the result justifies action.
OT and ICS visibility
AI can help classify devices, infer communication relationships, and identify changes in an industrial environment where asset records are incomplete. This is valuable only if the inferred inventory is clearly labeled as inferred, validated against engineering records, and protected from poisoning or unauthorized changes. An incorrect model-generated asset map can be more dangerous than an acknowledged gap because operators may rely on it during an outage.
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Incident-response decision support
An AI system may assemble a timeline, summarize relevant procedures, identify affected assets, or present containment options. In an OT environment, that support should stop short of autonomous actions that could interrupt a process unless the action has been explicitly authorized, tested, and bounded by safety controls. The decision to isolate a controller, block a protocol, or change a process must remain with people who understand the operational consequences.
Assessment workflows and resilience
DOE also names automated assessment workflows and resilience. AI can help compare configurations with an approved baseline, identify stale accounts, prioritize remediation, or rehearse how a service might operate through degraded connectivity. These activities are strongest when the system produces evidence that a responsible person can review rather than a binary “secure” score.
Securing AI used in energy operations
AI-FORTS includes protecting AI systems that operate, control, or defend energy systems. That is a reminder that the detector itself becomes part of the attack surface. Its training data, model artifacts, interfaces, update process, hardware, and availability require controls comparable to other critical systems.
What traditional operations still do better than a detector alone
NIST’s electric-utility situational-awareness guidance describes an enduring requirement: correlate OT/ICS, enterprise IT, and physical-access events; capture and analyze real-time or near-real-time information; investigate the chain of events; and respond, mitigate, and share findings. Those functions do not disappear when an AI model is added.
Cross-domain context
A plant-network alert may be meaningless without a badge event, a scheduled maintenance record, a change-management ticket, or an enterprise identity event. Traditional SOC processes are designed to bring those sources together and to ask the people who own each system what actually happened. An AI model can assist correlation, but it cannot create telemetry that was never collected or resolve an ownership dispute by itself.
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Investigation and accountability
Operators must be able to reconstruct what occurred, what evidence was considered, who approved a response, and which changes were made. Analysts provide skepticism, domain knowledge, and the ability to challenge an output. In safety-critical environments, that accountability is an operational control, not administrative overhead.
Response under imperfect conditions
Critical infrastructure may have legacy equipment, limited bandwidth, intermittent connectivity, or strict maintenance windows. A traditional response plan can specify manual procedures, out-of-band communications, local controls, and recovery priorities when a centralized service or model is unavailable. AI should fit into those plans, not become a single point of failure.
AI versus traditional operations: the decision dimensions
The following framework compares capabilities rather than marketing categories. A local pilot should test each question with representative data and the people who operate the affected process.
| Decision dimension | Questions for traditional operations | Questions for an AI capability | What a responsible evaluation requires |
|---|---|---|---|
| Detection coverage | Which OT, IT, physical-access, and vendor-access sources are monitored? Which assets remain blind spots? | Does the model add coverage for behaviors that rules cannot express, or only produce another view of the same telemetry? | Map data sources and known gaps; test whether new findings are actionable. |
| Context and correlation | Can analysts link events across domains and reconstruct a chain of events? | Can the system show the evidence and relationships behind a finding? | Use scenarios that include maintenance, outages, identity events, and process changes. |
| Alert quality and workload | How many alerts are investigated, escalated, closed as benign, or left unreviewed? | Does prioritization improve attention without hiding low-frequency, high-consequence events? | Establish a local baseline; collect disposition and time-to-triage data. No universal false-positive or alert-fatigue reduction rate is established for critical infrastructure. |
| Safety and availability | What monitoring and response actions are proven not to disrupt the process? | Could collection, model updates, inference load, or automated actions affect timing, reliability, or safe operation? | Test passively first, define maintenance windows, and require a safe fallback. |
| Explainability and authority | Can an analyst explain why a rule fired and who can approve a response? | Can users inspect relevant features, evidence, uncertainty, and model version? | Keep humans accountable for consequential decisions and record approvals. |
| Data and model assurance | Are logs protected from tampering, access abuse, and loss? | Are training and output data, model artifacts, interfaces, and update paths protected against poisoning, evasion, extraction, and unauthorized change? | Control provenance, access, integrity checks, versioning, and rollback. |
| Integration and resilience | Can existing tools and procedures operate during network or service degradation? | What happens when the model, cloud connection, API, or supporting data is unavailable? | Exercise degraded modes and prove that core detection and response still function. |
| Governance and evidence | Are use cases, baselines, acceptance tests, and review owners documented? | Is there a defined business case, validation plan, drift monitoring, and retirement or rollback process? | Approve expansion only after local evidence meets pre-set criteria. |
Can AI monitor OT and ICS safely?
It can be introduced safely only when the deployment is designed around OT performance, reliability, and safety requirements. OT directly interacts with the physical environment; a security control that is harmless in an office network can introduce unacceptable risk in a control system.
Start with observation, not intervention
A bounded pilot should begin with read-only collection and analysis. Avoid placing an untested agent on a controller, changing traffic flows, or allowing a model to send commands. Confirm where data is collected, how it traverses zones, what happens if the collector fails, and whether the monitoring path can be removed without affecting the process.
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Separate detection from authority
Design the system so an alert, recommendation, or generated playbook does not automatically become a control action. Require an identified analyst and an OT-qualified approver for isolation, blocking, configuration changes, or any action that could alter process behavior. Record the evidence and approval used for each consequential action.
Test maintenance and update paths
Model updates, feature changes, sensor firmware, and connector changes can alter behavior even when the process itself is unchanged. Use a change window, version identifiers, pre-deployment tests, and a rollback package. Define who can approve an update and how quickly the prior version can be restored.
Plan for degraded operation
Specify the manual or rule-based procedure used when the model is unavailable, uncertain, or producing implausible results. The fallback should cover loss of connectivity, corrupted data, a suspected model compromise, and a decision to suspend AI outputs during an incident.
AI introduces its own security and assurance risks
NIST identifies ordinary information-system risks alongside risks involving training data, output data, and the software and hardware that support AI. It also notes that current frameworks do not comprehensively cover the full AI attack surface. Relevant risk classes include:
- Evasion: an attacker crafts inputs that avoid detection while remaining operationally plausible.
- Data poisoning: manipulated training or reference data shifts the model’s understanding of normal behavior.
- Model extraction: repeated queries or access to artifacts may reveal a model or sensitive decision logic.
- Membership inference: outputs may disclose whether particular records appeared in training data.
- Availability failure: resource exhaustion, connectivity loss, or service compromise prevents timely inference.
- Integrity and provenance failure: an unauthorized model, feature pipeline, prompt, or connector changes the result without an obvious system error.
- Confidentiality exposure: sensitive process data, credentials, diagrams, or incident details may be sent to an inappropriate service or retained longer than intended.
- Automation bias: staff may accept a confident-looking recommendation without checking whether the evidence is complete.
These are identified classes of risk, not a claim that a particular detector has been compromised. The appropriate response is to define what evidence the system may access, isolate sensitive interfaces, authenticate every administrative action, monitor model and data changes, and make uncertainty visible to users.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Governance guidance and standards status
Joint OT guidance
Multi-agency OT guidance covering machine learning, LLM-based AI, AI agents, statistical modeling, and logic-based automation organizes its advice around four principles:
- Understand AI, its intended use, and its risks.
- Assess the specific OT use case, data, vendors, and longer-term integration issues.
- Establish governance and assurance.
- Embed safety and security practices, oversight, transparency, and incident-response planning.
The guidance calls for continuous monitoring, validation, and refinement. The joint authoring agencies, including CISA and Australia’s ASD’s Australian Cyber Security Centre, state: “The authoring agencies encourage critical infrastructure owners and operators to review and follow this guidance to achieve a more balanced approach to integrating AI into their OT environments and to continuously monitor, validate, and refine their AI models.” It was published on December 3, 2025.
NIST AI Risk Management Framework
NIST AI RMF 1.0 is voluntary and is being revised. NIST released a concept note on April 7, 2026, for a profile addressing trustworthy AI in critical infrastructure. A concept note is not a finalized sector standard or a binding requirement. Organizations should map applicable national, sectoral, safety, privacy, and resilience obligations separately.
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As of September 21, 2026, NIST SP 800-82 Rev. 4 was an initial public draft, not a final standard. The draft addresses OT’s distinctive performance, reliability, and safety requirements, expands asset-management and monitoring/detection guidance, and aligns its structure with NIST Cybersecurity Framework 2.0. NIST listed November 30, 2026, as the comment deadline. Verify its status before treating any provision as current final guidance.
How to run a safe, useful AI detection pilot
A pilot should answer a defined operational question, not merely demonstrate that a model can produce scores. The following sequence keeps the evaluation bounded and evidence-based.
- Choose one use case. Examples include identifying unauthorized engineering access, prioritizing anomalous remote activity, or correlating OT and physical-access events. Do not begin with a promise to “secure the whole plant.”
- Define the safety boundary. List affected processes, prohibited actions, maintenance windows, network zones, data classifications, and the people who can stop the pilot.
- Document the current baseline. Record existing telemetry, detection rules, alert volumes, investigation time, escalation paths, missed-visibility areas, and fallback procedures. If a value is not measured today, mark it as unknown rather than inventing a baseline.
- Inventory and qualify the data. Identify source owners, timestamps, retention, clock synchronization, missing fields, inferred assets, and whether records can be altered. Include the work orders and process context needed to distinguish planned changes from attacks.
- Test in a representative but safe environment. Use a laboratory, digital twin, replayed data, or an isolated segment where feasible. Include normal variation, maintenance, outages, degraded connectivity, and known suspicious sequences.
- Set human-review rules. Define who validates an alert, what evidence must be shown, when an OT operator must be consulted, and which actions always require approval.
- Measure outcomes locally. Compare the pilot with the existing workflow using pre-agreed measures. Do not treat a vendor’s generic benchmark or a model confidence score as proof of operational value.
- Exercise failure and fallback. Disconnect the model, corrupt a feed, introduce stale data, roll back a model, and suspend automated recommendations. Confirm that analysts can continue with established procedures.
- Review drift and change. Establish owners for retraining, threshold changes, new assets, process changes, and model retirement. Require a new validation cycle after material changes.
- Decide whether to expand. Expand only if the added signal is useful, the safety case remains acceptable, the workload is manageable, and the fallback and governance controls work under test.
Metrics to collect before claiming improvement
No attributable public statistic establishes that AI has a particular detection rate, false-positive rate, cost saving, or response-time advantage over traditional operations in critical infrastructure. A utility, manufacturer, or other operator should measure its own results under stated conditions.
| Measure | How to collect it | Why it matters |
|---|---|---|
| Coverage | List monitored assets, protocols, identities, physical-access feeds, and known blind spots before and after the pilot. | Shows whether the system adds meaningful visibility rather than duplicate telemetry. |
| Alert disposition | For each alert, record validated incident, benign change, duplicate, insufficient evidence, or unresolved status. | Separates useful detection from volume. |
| Time to triage | Measure elapsed time from alert creation to an informed disposition, with the operating context recorded. | Tests whether prioritization helps analysts. |
| Investigation completeness | Check whether analysts can reconstruct affected assets, identities, commands, process state, and the event sequence. | Prevents a fast but incomplete investigation from being counted as success. |
| Operational impact | Record latency, resource use, connectivity effects, maintenance exceptions, and any process disturbance. | Captures safety and availability costs that a SOC-only metric misses. |
| Explainability and approval | Audit whether each consequential recommendation includes evidence, model version, uncertainty, reviewer, and decision. | Supports accountability and later investigation. |
| Resilience | Time and observe recovery after feed loss, model failure, rollback, or isolation from external services. | Shows whether the capability fails safely. |
| Drift | Track changes in data quality, asset population, process behavior, and alert dispositions over time. | Detects when a model’s assumptions no longer match the plant. |
Common failure modes and how to correct them
| Symptom | Likely cause | Corrective action |
|---|---|---|
| Alert volume rises but investigations do not improve | The model is learning normal variation without enough process or maintenance context. | Add authoritative context, narrow the use case, tune thresholds with OT operators, and measure dispositions rather than raw alert count. |
| A planned outage is repeatedly flagged as hostile | Schedules and work orders are missing, late, or not linked to telemetry. | Integrate change records, label exceptions, and test the full maintenance workflow. |
| Analysts cannot explain a high-priority score | The output hides features, evidence, uncertainty, or model version. | Require an evidence view and confidence qualification; block consequential automation until reviewability is restored. |
| The model performs well in a lab and poorly after deployment | Training data did not represent the plant’s assets, process states, or degraded conditions. | Expand representative test data, document assumptions, validate in stages, and maintain a fallback. |
| Model updates create unexpected changes | Versioning, approval, or rollback controls are weak. | Use signed artifacts, staged deployment, change records, acceptance tests, and a tested prior version. |
| The AI service becomes unavailable during an incident | The design depends on a cloud connection, centralized API, or a single data pipeline. | Exercise local rules, manual procedures, cached evidence, and out-of-band communications. |
| Operators trust the recommendation despite contradictory evidence | Automation bias or unclear responsibility. | Train users, display uncertainty, require evidence review, and assign explicit approval authority. |
| A visibility map contains unsafe assumptions | Inferred assets or relationships were treated as confirmed engineering facts. | Mark confidence, validate with asset owners, and keep authoritative records separate from model inferences. |
When should an organization augment, delay, or reject AI?
Augment existing operations when
- The use case is narrow enough to define a safety boundary and a measurable baseline.
- Relevant OT, IT, physical-access, maintenance, and identity data can be joined with acceptable integrity and latency.
- Analysts and OT operators can inspect the evidence and remain accountable for decisions.
- The deployment has a tested fallback, rollback process, and owner for model and data changes.
- The expected benefit is better prioritization, coverage, or investigation support rather than an unsupported promise of autonomous defense.
Delay deployment when
- Asset ownership, time synchronization, data provenance, or change records are too incomplete to interpret alerts safely.
- The organization cannot staff human validation or define who may authorize a response.
- The model cannot operate during expected connectivity or maintenance conditions.
- There is no way to test updates, detect drift, or restore the prior operating mode.
Reject a proposed capability when
- It requires unapproved control access or makes irreversible changes without a human decision.
- Its supplier cannot explain data handling, model updates, logging, retention, administrative access, and incident notification.
- Its claimed performance is based on an unrelated environment, an undisclosed test, or a score that cannot be connected to operational outcomes.
- It adds a critical dependency without a credible degraded-mode procedure.
Bottom line for critical-infrastructure leaders
AI-powered detection is best evaluated as one component of a resilient SOC. It may improve hunting, anomaly analysis, cross-domain correlation, assessment work, and incident-response decision support, but those opportunities remain use-case dependent. Traditional operations still provide the telemetry stewardship, investigation, OT context, human judgment, safety controls, and recovery procedures that make a detection meaningful.
The defensible adoption test is local and operational: define a bounded use case, establish a baseline, test with representative OT conditions, require human approval for consequential actions, protect the model and its data, monitor drift, and prove that the organization can continue safely when the AI capability is wrong or unavailable. Until those conditions are demonstrated, “AI versus traditional” is the wrong choice. The safer design is accountable traditional operations with narrowly governed AI assistance.
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