Threat detection was designed for the disorganised – and that’s why it keeps missing the truly dangerous.

Traditionally, we built if-this-then-that logic to catch opportunistic trespassers. If a beam is broken, the siren sounds. While this remains effective for petty fraud, it has become a minor speed bump for modern adversaries.

The Sophistication Mismatch

But adversaries have reorganised. The landscape no longer revolves around random insiders or script kiddies.

Today, the prevalence is shifting toward Adaptive Threats. These are networked, organised entities – from crime syndicates to foreign intelligence services – that leverage AI and disciplined tradecraft to blend into the noise of legitimate business.

For organisations managing high-stakes assets, relying on out-of-the-box detection is no longer just a gap; it is a liability.

The Relationship: High-Stakes Assets and Adaptive Threats

Sophistication follows the money. Adaptive threats focus their resources where the payoff justifies the complexity.

We must define High-Risk through this direct relationship:

• Adaptive Threats: Intelligent adversaries who refine tactics continuously to bypass static defenses.
• High-Stakes Assets: Organisations whose information, systems, or capital (IP, PII, or Critical Infrastructure) justify a highly resourced intrusion.

If you own the asset, you are the target.

Traditional Fraud Controls Catch Thieves. Oceans Eleven Catches You

The Three-Tier Detection Framework

To counter this, high-risk organisations need three distinct detection methodologies working in concert:

Tier 1: Rule-Based Detection (The Known-Knowns)

• Methodology: Relies on deterministic triggers: If X occurs, then alert.
• Target: Opportunistic or disorganised actors.
• The Gap: Easily mapped and evaded by an adaptive actor who understands your thresholds.

Tier 2: Anomaly-Based Detection (The Unknown-Knowns)

• Methodology: Establishes a statistical baseline of normal behavior and flags deviations.
• Target: Evolving threats and novel behaviors.
• The Gap: Sophisticated AI/ML is rare (lt;10% adoption). In Australia, only 34% of organisations currently use UEBA effectively, meaning most cannot yet detect subtle deviations before damage occurs.

Tier 3: Scenario-Based Detection (The Adaptive Edge)

• Methodology: Uses sequential logic to model a specific threat story (Event A – Event B – Event C).
• Target: Multi-stage tradecraft, complex fraud, and precursors to physical sabotage.
• The Gap: This requires advanced threat modeling. Currently, you could count the number of people in Australia proficient at this on 2-4 hands.

“Typologies” Sound Boring – But They Could Save Your Business Millions

Bridging the Capability Gap

Most vendor pitches focus on feature checklists, not strategic frameworks.

For the high-risk organisation, detection cannot be a plug-and-play purchase. You cannot afford to realise in year two that your chosen system lacks the correlation logic required to detect a multi-stage attack.

Detection as a Holistic Capability

Effective detection is not a software toggle. You must bring five components together at the right time:

• Skilled People: Experts who can turn intelligence into detection logic.
• Right Data: High-fidelity telemetry from cyber, physical, and financial sources.
• Mature Processes: A workflow moving from Threat Modeling to Model Deployment.
• Integrated Technology: Systems capable of correlating all three tiers.
• Governance: Oversight to ensure accuracy without disrupting operations.

Understanding Insider Threat Modelling for Accurate Detection

The Takeaway

Detection maturity isn’t optional for those guarding national or financial crown jewels.

Relying solely on basic, rule-based detection is a choice to wear the risk of a major loss.

Build capability – not complacency. Align your methodology to the actor you are actually fighting.

Further Reading

• Curwell, P. (2022). “Typologies” Sound Boring – But They Could Save Your Business Millions
• Curwell, P. (2025). Traditional Fraud Controls Catch Thieves. Oceans Eleven Catches You
• Curwell, P. (2025). Understanding Insider Threat Modelling for Accurate Detection

As published on LinkedIn.

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