The Blind Operator Scenario During an OT Breach

In OT cybersecurity, a breach (or a suspected breach) creates a state of uncertainty regarding process integrity.

This is the Blind Operator scenario: a situation where the control system indicates no unexpected change in asset behavior, but there is no independent method to confirm that this reported state reflects the physical reality of the asset.

For the CISO, this is a “source of truth” problem. When the integrity of the control layer is in question, the digital data used to manage the process or asset becomes an “unknown unknown.”

The Post-Breach Verification Gap

Once a breach is suspected, the organization faces a critical visibility deficit. Because network-based monitoring relies on the data packets sent by the devices themselves, it results in a total loss of visibility. For critical infrastructure providers facing sophisticated cyber threats, this isn’t just a localized blind spot; it is a complete blind view where the operator sees exactly what the attacker wants them to see, while the actual physical state of the asset remains entirely hidden.

This gap is exacerbated by the fact that most OT communication protocols still lack encryption or authentication. This allows an attacker to employ Man-in-the-Middle tactics with relative ease, resulting in a complete de-coupling of the digital reporting layer from the physical process. 

This vulnerability introduces two primary risks:

Potential for Controller-Level Data Spoofing: If an attacker manages to compromise the PLC logic, they gain the ability to decouple reported values from physical reality. Because the resulting traffic still adheres to authorized network protocols and comes from trusted devices, the monitoring tool may continue to validate the connection as routine, even if the process data within it has been falsified.

The Risk of an HMI Deception Loop: There is a danger that an attacker could freeze the operator’s view by falsifying data at the source. In this scenario, the HMI would report steady performance indicating a Business as Usual scenario, but masking an active attack while the physical equipment is moving to failing state without triggering a network-level alarm.

Without an out-of-band source of truth, there is no way to verify whether the physical process is operating within actual tolerances or if there is an unreported change in asset behavior, such as a pressure spike or mechanical vibration. The operator is blinded because the reported data is no longer verifiable.

Uncertainty at the Process Level: The Remediation Lag

The Blind Operator scenario is characterized by a disconnect between network level reports and physical reality during a security event. In this state, the data displayed on the HMI remains within expected bounds, but the information cannot be used to verify the actual state of the asset.

This disconnect creates a dangerous operational impasse: if you do not know what is actually happening at the plant asset level, you cannot safely remediate the incident. This lack of physical certainty leads to severe lags between the initial breach and final remediation.

 According to the SANS 2025 State of ICS/OT Security Survey:

• Containment vs. Recovery: While 60% of incidents are contained within 48 hours , remediation (which involves eradicating the threat and recovering operational integrity) often stretches into days or weeks.
• Extended Timelines: Nearly 20% of organizations require more than a month to fully remediate an incident.
• Recovery Complexity: Only 14% of respondents feel fully prepared for emerging threats, largely because recovery requires more than just digital containment; it requires proving the process is safe to resume.

For example, an operator might see a “Normal” 50% capacity reading on their screen while the physical reality is a pump sustaining a pressure spike. Because the monitoring is dependent on the same control logic and network infrastructure under suspicion, the organization lacks a secondary, independent path to confirm that the mechanical process matches the digital record. 

At Level 0, the actual condition of the machine remains an “unknown unknown.”

Establishing an Independent Source of Truth: SigaML²

To limit this uncertainty and accelerate recovery, a detection strategy must include a path that is structurally independent of the control network. 

SigaML² addresses this by analyzing data gathered from all levels of the Purdue Model (Levels 0-4) to support critical decision-making throughout the NIST Incident Response (IR) framework. By moving the detection point to the unprogrammable Level 0 layer, SIGA provides a “source of truth” that remains effective even when traditional IDS tools are blinded by an ongoing attack.

Preparation (SigaPAS): Uses process-specific threat scenarios to inject simulated anomalies, allowing teams to safely train on realistic attack expressions and develop IR playbooks.

Detection & Analysis (SigaGuard/SigaGuardX): Applies Machine Learning to identify discrepancies between the digital and physical layers. This specifically addresses the HMI Deception Loops and Controller-Level Spoofing mentioned earlier; by comparing data from Levels 1-4 to the raw electrical signals at Level 0, SIGA identifies if the ‘authorized’ network traffic has been falsified to mask a physical attack.

Containment & Recovery Support: Provides an out-of-band Decision Support System. By delivering unfiltered Level 0 data, SigaML²  helps CISOs decide whether to shut down, disconnect, or continue operations with caution based on actual asset behavior.

Conclusion: Ensuring Operational Continuity

The Blind Operator scenario demonstrates that during a cyber incident, network-level visibility alone is insufficient. If the monitoring strategy relies on the same data stream that is under suspicion, there is a risk of uncontrolled asset behavior and extended remediation timelines.

Integrating Process Level (Level 0) signal analytics ensures that process integrity can be independently verified. This provides the evidence needed to maintain operational continuity and make informed decisions, even when the control layer reports no noticeable change in performance.