ADT-4 Benchmarking and Evaluation Summary

Introduction

ADT-4 was built to operate inside environments where correctness and interpretability are critical. Evaluations for this model do not focus on conversational benchmarks. Instead, they target the tasks institutions rely on: threat detection, autonomous defense, long form reasoning, compliance mapping, and incident reconstruction.

This post outlines the core benchmarking areas and summarizes the performance of ADT-4 across each one.

Reasoning Stability Benchmarks

Reasoning stability measures how well ADT maintains a clear, coherent line of thought when evaluating complex or multi step security signals.

Multi Step Consistency

ADT demonstrates strong consistency across extended reasoning chains. The model retains earlier context and avoids contradictory conclusions during long investigations.

Conflict Resolution

When receiving mixed or ambiguous data, ADT selects the most evidence based conclusion and clearly identifies uncertainty when needed.

Narrative Reconstruction

ADT can rebuild the sequence of events behind an incident by connecting logs, behaviours, and metadata into a coherent storyline.

Threat Detection and Analysis Benchmarks

Evaluations for threat detection were performed using historical incident data, synthetic attack sequences, and contemporary attack pattern samples.

Detection Accuracy

ADT maintains high accuracy across brute force attacks, privilege escalation patterns, injection attempts, reconnaissance activity, and behavioural anomalies.

False Positive Control

The model prioritizes interpretability over raw sensitivity. By evaluating signals with context, ADT avoids unnecessary escalations and reduces noise for analysts.

Threat Categorization

ADT identifies attack types with clear explanations. When multiple possibilities exist, the model ranks likely scenarios and provides justification for each.

Compliance and Regulatory Evaluation

Compliance evaluations measure how well ADT interprets framework requirements and maps evidence to obligations.

Framework Coverage

ADT provides strong performance across African regulatory frameworks including CBN, NDPR, POPIA, and Kenya DPA. It also maintains clarity when evaluating global frameworks such as GDPR, NIST CSF, PCI DSS, HIPAA, and ISO standards.

Scoring Consistency

Scores remain stable across repeated assessments. The model avoids fluctuation when interpreting fixed evidence.

Evidence Mapping

ADT links observed controls, logs, and configurations to exact requirement clauses with high interpretability.

Adversarial and Stress Testing

These tests evaluate how ADT performs under pressure and in presence of intentionally misleading inputs.

Obfuscated Input Handling

ADT identifies and interprets malformed or partially altered signals without degrading into unsafe or speculative conclusions.

Adversarial Pattern Resistance

The model remains stable when facing deceptive traffic, inconsistent log streams, or contradictory behaviours.

High Load Stability

Under high volume ingestion, ADT preserves reasoning quality and avoids sudden behavioural changes.

Operational Reliability

Operational reliability measures how well ADT performs as part of day to day infrastructure.

API Performance

All ADT APIs exhibit consistent response times under normal and elevated load, suitable for production environments.

Agentless and Agent Based Ingestion

Both ingestion paths support continuous real time analysis with minimal latency.

Incident Lifecycle Quality

ADT produces consistent timelines, structured evidence, and actionable summaries for incidents across all severity levels.

ADT-4 demonstrates high stability in reasoning, dependable analysis under stress, and strong interpretability for regulated environments. The model shows consistent performance across both security and compliance tasks. These evaluations confirm that ADT-4 is suitable for institutional use across cloud and on premise environments.