For decades, signature-based tools have been the frontline of industrial cybersecurity. Antivirus platforms, firewalls, and intrusion detection systems rely on a simple principle: if you’ve seen a threat before, you can stop it again.
But here’s the problem: the most dangerous threats in Industrial Control Systems (ICS) today aren’t the ones we’ve seen before. They’re the ones we haven’t.
Signature-based security is increasingly blind to modern threats. Zero-days, living-off-the-land techniques, and insider-driven compromises don’t carry signatures. And in many industrial environments, they slip through undetected until it’s too late.
The Signature Gap: Why Traditional Detection Misses the Mark
Signature-based security is reactive by design. It depends on predefined threat patterns; malware hashes, known behaviors, file indicators to catch what’s already known.
But in ICS networks, the threat landscape has shifted. Attackers aren’t always deploying malware or reusing known exploits. Instead, they’re blending in: repurposing trusted tools, mimicking normal traffic, or moving laterally across flat networks without ever triggering a signature.
Some high-profile examples:
- Snake Malware / Turla (Uncovered 2023): A sophisticated espionage tool used by APT groups for over a decade. It evaded detection for years by hiding in plain sight, avoiding known malware signatures. [Source: CISA Joint Advisory on Snake Malware]
- Industroyer2 (2022): This malware targeted Ukrainian energy infrastructure by imitating ICS protocols. Signature tools struggled because the malware didn’t exhibit typical malicious behavior, it just looked like legitimate grid commands. [Source: ESET Threat Report: Industroyer2]
- TRITON/Trisis (2017): This attack targeted safety instrumented systems (SIS). It bypassed conventional detection by exploiting zero-day vulnerabilities and behaving like trusted engineering software. [Source: Trendmicro Analysis of TRISES]
In all these cases, signature-based tools failed. Not due to poor design but because they were never built to detect the unknown.
Behavioral Detection 101: What AI Sees That Signatures Don’t
Unlike signature tools, AI doesn’t wait for known indicators. It builds a baseline of what normal behavior looks like inside an ICS network then alerts when something deviates.
What does that mean in practice:
- Device Behavior Monitoring: AI can detect when a PLC suddenly starts sending data to an unfamiliar endpoint even if the traffic looks legitimate.
- Protocol Anomaly Detection: AI recognizes protocol use that falls outside normal operational patterns (e.g., an unusual Modbus command sequence at 2 a.m.).
- User Behavior Analytics: AI spots credential misuse like an operator logging into systems they’ve never accessed before, from a new IP.
This is where behavioral detection thrives: identifying how things happen, not just what is happening.
Use Cases in ICS: Catching What Others Miss
Here are real-world ICS attack scenarios where AI-driven behavioral detection outperforms signature-based tools:
- Lateral Movement Across Flat Networks
In many ICS environments, segmentation is weak. Once an attacker compromises one device, they can move laterally often without triggering any alarms. AI detects these subtle shifts in east-west traffic. - Asset Impersonation
Attackers sometimes spoof the identity of legitimate devices, blending into exfiltrate data or execute unauthorized commands. Signature tools can’t differentiate real from fake if the traffic matches expectations. Behavioral AI can. - Insider Threats
A disgruntled engineer logs into a SCADA system and alters thresholds. No malware involved, no signature triggered yet the behavior is dangerous and unauthorized. AI spots the deviation from the user’s historical activity.
AI Readiness in OT: Preparing for the Shift
To get value from AI-driven detection, OT environments need to do some groundwork:
- Establish Accurate Baselines
AI needs a sense of “normal.” That means consistent, clean operational data and clarity on scheduled activities. - Map Critical Processes
Know what’s critical to production. If AI flags anomalies tied to core functions, it helps prioritize what gets addressed first. - Bridge IT-OT Context Gaps
AI works best when it can pull from both network telemetry and process context. OT teams must collaborate with IT to share insights on what’s expected behavior.
Offline vs. Cloud: Detecting Threats in Air-Gapped Environments
One common challenge: many industrial environments are air-gapped or isolated for safety. This limits the use of cloud-based analytics and real-time threat feeds.
But AI doesn’t require the cloud to function. Models can be trained offline and deployed locally. As long as they receive contextual input; network logs, asset behavior, historical patterns they can deliver value in air-gapped environments.
This makes behavioral AI one of the few modern security approaches that can scale to isolated ICS environments without sacrificing efficacy.
Final Thought: Resilience Requires More Than Recognition
Signature-based tools still have a place in layered defense but they’re no longer enough. In 2025, the most effective ICS security strategies don’t just recognize known threats; they adapt to evolving behaviors.
If your detection strategy depends only on what the world has already seen, it’s time to evolve. Because the threats that matter most are the ones your tools don’t recognize yet.
