How AI and ML Can Detect Anomalous Subsea Activity

Detecting Anomalous Subsea Activity Is a Decision Problem

Subsea infrastructure does not fail loudly.

Cables, pipelines, and offshore energy assets operate in environments where disturbances are subtle, indirect, and often ambiguous. Most activity near subsea infrastructure is benign: fishing, maintenance, transit, environmental effects, or normal seabed interaction.

The challenge is not the absence of data. It is deciding when behavior deviates meaningfully from normal.

This is where artificial intelligence and machine learning add value. It is not by “finding threats,” but by supporting judgment under uncertainty.

Why Subsea Anomaly Detection Is Hard

Unlike air or surface domains, subsea environments impose constraints that make traditional surveillance ineffective at scale:

• Limited direct visibility
• Sparse and heterogeneous sensors
• Environmental noise dominates signals
• Ground truth is rarely available in real time

As a result, most subsea incidents are identified after the fact: during inspection, repair, or forensic analysis.

The objective of AI/ML is not to eliminate uncertainty, but to reduce surprise.

From Raw Signals to Behavioral Context

AI and ML are most effective when applied to patterns, not individual events.

In subsea monitoring, this typically involves combining multiple weak signals into a coherent picture:

• Environmental measurements (currents, waves, seabed motion)
• Acoustic or vibration data from distributed sensors
• Vessel movement patterns from surface-domain data
• Historical baselines tied to location and season

Individually, these signals are noisy. Together, they form behavioral context.

Machine learning excels at learning what “normal” looks like and identifying when reality begins to drift.

Anomaly Does Not Mean Threat

A critical distinction in responsible defense and infrastructure protection is this:

An anomaly is not an accusation.

Most detected deviations resolve to benign explanations:

• Weather-driven effects
• Operational activity
• Sensor artifacts
• Maintenance or repair work

AI/ML systems must therefore prioritize classification with uncertainty, not binary conclusions.

The output is not:

“This is hostile.”

It is:

“This behavior deviates from historical norms in these specific ways.”

That difference matters.

Confidence-Aware Detection, Not Automated Escalation

Well-designed AI/ML systems support a graduated response:

1. Detection
   Identify statistically meaningful deviations from baseline behavior.

2. Characterization
   Describe how the deviation differs: duration, intensity, spatial pattern.

3. Confidence estimation
   Quantify uncertainty and likelihood ranges rather than issuing absolutes.

4. Explainability
   Preserve traceability to underlying signals and assumptions.

5. Decision support
   Enable human operators to choose observation, verification, or escalation.

In most cases, the correct outcome is continued monitoring.

Why Learning Over Time Matters

Subsea environments are not static. Seasonal effects, climate variability, operational changes, and infrastructure aging all shift baselines.

AI/ML systems improve not because they are retrained constantly, but because they:

• Incorporate new verified outcomes
• Refine what constitutes normal behavior
• Reduce false positives without masking real change
• Maintain institutional memory beyond individual operators

Each resolved anomaly sharpens future judgment.

Alignment With Real-World Operational Practice

This approach mirrors how other safety- and security-critical domains operate:

• Aviation monitors deviations long before incidents occur
• Finance flags anomalies without assuming fraud
• Energy operators investigate irregular readings before halting production

In each case, early detection supports restraint, not reaction. Subsea infrastructure protection follows the same logic.

The Role of AI and ML in Subsea Protection

AI and ML do not replace operators, regulators, or decision-makers. They provide something more valuable:

The ability to see change early, understand it in context, and decide deliberately.

That capability supports both escalation and restraint, making both defensible.

In environments where action is costly and mistakes are asymmetric, knowing when not to act is as important as knowing when to intervene.

Detecting anomalous subsea activity is therefore not a surveillance problem. It is a decision problem. And AI/ML are tools to make that decision clearer, calmer, and better informed.