Research - Robusto Industry Domain Research Report

February 2026.

Robustness For Real-World AI

Ship AI that survives production pressure.

Cortexa Labs develops Robusto, an AI diagnostic and maintenance tool that stress-tests models, finds failure points, and generates targeted synthetic data to improve real-world robustness.

AI Models, Data Types, and Adversarial Attack Landscape across critical sectors. If you are evaluating reliability tooling for production AI, this hub shows how Robusto addresses real attack surfaces by vertical.

Primary Target Industries

Cybersecurity ML and Fintech ML are our two priority verticals. Both face adversarial pressure that already maps to regulator and procurement asks, and both have ML teams in production today.

Cybersecurity ML

Detection, EDR, SOC, UEBA, and threat-intel models. Adversarial inputs, alert evasion, prompt injection, and data poisoning attack surface.

Open Cybersecurity ML brief

Fintech ML

Fraud detection, AML, KYC, credit decisioning, and synthetic-identity defense. Regulated under EU AI Act Annex III and SR 11-7 model risk.

Open Fintech ML brief

Executive Summary

We mapped model stacks, modality footprints, and adversarial vectors in cybersecurity, fintech, medical, robotics, solar/energy, space, and surveillance deployments. Cybersecurity ML and fintech ML lead the priority list because their attack surface, regulatory exposure, and procurement urgency are converging right now.

Each domain segment explains where failures happen, what attacks are documented, and which Robusto capabilities reduce practical deployment risk and compliance exposure.

Our robotics track extends that view into simulation-heavy systems, where policy learning, sensor fusion, and field transfer create a different reliability problem than standard software-only ML.

Key Findings

Cybersecurity ML: Cybersecurity copilots and detection models face prompt injection, alert evasion, data poisoning, and model extraction risk.
Fintech ML: Fintech fraud and AML pipelines face evasion, poisoning, model extraction, and synthetic-identity attack pressure.
Medical imaging CNNs show high sensitivity to pixel-level perturbations and one-pixel style attacks.
Robotics/autonomous stacks are exposed to digital and physical sensor attacks, including LiDAR and GPS spoofing.
Solar and smart-grid forecasting systems are vulnerable to false data injection and regression-targeted perturbations.
Space AI systems inherit adversarial risk amplified by edge constraints, latency, and limited human intervention.
Surveillance models can be bypassed through physical patches, adversarial clothing, and temporal perturbations.

Research Verticals

Primary:

Other researched verticals:

Cross-Domain Analysis

Industry Domain Landscape: Detailed model, modality, and adversarial exposure mapping across six verticals with domain-specific opportunities.
Cross-Domain Attack Taxonomy: Unified adversarial taxonomy spanning evasion, poisoning, extraction, inversion, physical attacks, and sensor spoofing.
Product Roadmap Recommendations: Core capability priorities, domain rollout tiers, and strategic differentiation guidance for Robusto platform buildout.