A Process Oriented Upgrade to Obsolete Incident Response Plabyooks

In Operational Technology (OT) environments, Incident Response (IR) timelines are measured against operational uncertainty. The longer it takes to determine whether cyber activity has affected the physical process, the harder it becomes to decide whether to continue running, isolate equipment, degrade operations, or trigger an emergency shutdown.

This is the fundamental divide between enterprise IT and industrial OT.

Because OT systems control physical operations, a cyber event instantly threatens safety, environmental compliance, and production availability. When a physical process is compromised, defenders cannot rely solely on software logs, network packets, or even PLC-reported values to decide what to do next.

The combination of three distinct forces is intensifying the pressure on CISOs to eliminate this window of uncertainty:

The Collapse of the “Air Gap.”

Today, industrial networks are fundamentally hyper-connected. Remote vendor support, data historians, predictive maintenance, and cloud analytics have created dozens of pathways into control environments. Secure OT connectivity is not an exception handled on a case-by-case basis; it is a complex architectural design challenge that must be engineered from the ground up.

The Expanding Footprint of Visible, Exploitable Industrial Assets

The industrial attack surface is highly visible to adversaries.

This trend is explicitly documented in the August 2025 study, “Analysis of Publicly Accessible Operational Technology and Associated Risks” that identified nearly 70,000 publicly exposed OT devices globally across protocols like Modbus TCP, EtherNet/IP, and Siemens S7.

Their findings confirmed that because these devices are connected to the open web, they openly return identifying configuration data to any external scanner. This makes them easily fingerprintable. Crucially, in many cases, these devices run critical, known vulnerabilities that have left them exposed from outside the trusted environment for years.

Compressed Threat Timelines via Automated AI

The time-to-exploit window is shrinking significantly because malicious threat processes are now becoming more automated.  By using AI-assisted reconnaissance, automated vulnerability analysis, coordinated multi-stage execution, adversaries are dramatically lowering the time and skill required to move from initial access to malicious action. The result:  human defenders have significantly less time to intercept a threat before it translates into physical process impact.

The Structural Pressure Point

This convergence has caused an inflection point for OT incident response.

The critical question for a CISO is not “What happened on our network?”

It is, “Has this cyber event altered the state of our physical process?”

As connectivity expands, malicious processes move at machine speed, and operational disruption becomes, and operational disruption becomes inevitable, the acceptable window for uncertainty has collapsed to near-zero.

The Real Toll of Operational Uncertainty

The financial and operational consequences of guessing wrong are severe. According to threat data from Dragos, among observed ransomware cases affecting industrial organizations, 25% forced a complete shutdown of the OT site, while the remaining 75% caused significant operational disruption.

For CISOs, the reality today is that the clock does not end with malware identification or host isolation. The clock only stops with a defensible operational decision: Do we safely continue, isolated segments, run in a degraded state, or hit the kill switch?

Obsolete Incident Response Playbooks

There are four distinct reasons why standard response playbooks fall short in this environment:

The Speed Mismatch (Human vs. Machine): Most Incident Response playbooks are linear, step-by-step instructions designed for human execution (e.g., triage, assess, approve, contain). Because automated attacks compress threat timelines into minutes, the response process lags behind the threat. By the time the initial steps of a playbook are completed, the threat has already progressed far beyond the initial point of detection.

The Data Dependency Bottleneck: The default requirement is that a threat must be validated before containment steps are executed. When there is an increase in the volume of attacks, it creates a bottleneck because validation is entirely dependent on software-layer data (logs, packets, and HMI screens). This reliance creates a operational lag; while defenders are waiting to compile and report what happened, the actual threat continues to progress unchecked.

The Operator Blindspot: A cyber playbook tells a defender how to isolate a network asset, but it cannot tell them what that physical asset is actively doing on the plant floor. Because the playbook lacks real-time physical context, defenders face a blind choice: execute the playbook step on the assumption that there is no physical impact or do nothing.

The Risk of Unnecessary Shutdowns: In IT, if a wrong decision is made to isolate a server it can be reconnected with minimal consequence. In OT, if a playbook tells you to isolate a critical PLC, you might accidentally trigger a catastrophic emergency shutdown or damage a multi-million dollar machine. Because containment actions may risks production availability, the standard institutional focus remains on avoiding steps with physical consequences. This operational focus can lead to a response bottleneck or a failure to act during an active incident

The Shift from Cyber Playbooks to Process Integrity Verification

Traditional Incident Response processes operate primarily at the software and network layers. With Siga, the execution of the response shifts from network forensics to verifying real-time process integrity. The portfolio combines SigaGuard (Level 0 hardware reading raw electrical signals out-of-band) and SigaGuardX (software correlating Purdue Levels 1–4 data with those Level 0 signals).

While process monitoring does not eliminate cyber-layer threat velocity or tool sprawl, it addresses core flaws of traditional playbooks in three specific ways:

• An Independent Path Through Data Chaos: When a high volume of alerts causes a software-layer bottleneck, SigaGuard provides an alternative pathway. Because it monitors raw electrical signals directly from sensors and actuators, the physical state of the machinery remains visible even if the network layer is entirely compromised or flooded.
• Exposing False Data Injection (FDI): While technically complex and less common, False Data Injection (FDI) remains a dangerous tactic where attackers alter network packets to show normal telemetry on operational screens while secretly modifying the physical process. SigaGuardX addresses this blind spot when paired with the SigaGuard hardware. The software uses machine learning to cross-reference upper-level digital data with Level 0 electrical signals, immediately flagging any discrepancy between software reports and physical reality.
• Enabling Defensible Operational Decisions: During a cyber incident, the decision-making process can stall because traditional playbooks offer no visibility into the physical industrial proceesses. Without that insight, executing a containment step risks triggering an unnecessary, costly emergency shutdown. By delivering objective proof of the machinery’s actual physical health, the SigaML² product suite provides the clarity needed to isolate a compromised network segment while safely maintaining production. isolate a compromised network segment while safely allowing the physical process to keep running.

Summary and Conclusion

Incident Response frameworks have not adapted to the acceleration in cyber attack vectors.

As attacks move at machine speed, traditional playbooks remain tethered to manual, and often compromised software-layer data. This gap creates a dangerous window of uncertainty that can leads to decision-making paralysis.

True resilience in OT requires a shift from network forensic analysis to real-time physical process integrity. By providing an un-hackable, out-of-band source of truth at the electrical signal level, SIGA removes the data dependencies and blind operator scenarios that stall response plans.

This provides cyber teams with the objective evidence required to move past the fear of accidental shutdowns, enabling immediate, defensible decisions to protect both the process and the bottom line.