Industry Domain Analysis - Research

Compare your domain stack against the documented threat landscape. This page maps model families, data modalities, and attack vectors to the concrete Robusto capabilities that matter during vendor selection.

Medical / Healthcare AI

Models

• Classification: ResNet-50, VGG-16, Inception-V3, DenseNet, EfficientNet, Covid-Net
• Segmentation: U-Net, nnU-Net, DeepLab, SegFormer
• Detection: Faster R-CNN, RetinaNet
• Clinical NLP: BioBERT, ClinicalBERT, Med-PaLM, GPT-4 (medical)
• Vision-Language: MedCLIP, BiomedCLIP, LLaVA-Med
• Generative: GAN pipelines (including StyleGAN variants)

Data Types

• X-ray, CT, MRI, ultrasound, OCT and retinal imaging
• Histopathology slides and dermatology imagery
• Clinical notes, EHR tabular/time-series, genomic signals
• Waveform data including ECG and EEG

Adversarial Attacks

• FGSM, PGD, BIM, one-pixel and universal perturbation attacks on medical imaging
• Segmentation mask manipulation for tumor and lesion pipelines
• Patch and color-jitter attacks against pathology workflows
• Prompt injection and hallucination induction in medical NLP/LLM pipelines
• Cross-modal attacks on medical vision-language systems

What You Gain With Robusto

• Medical-modality perturbation libraries (X-ray, CT, MRI, OCT)
• Automated robustness benchmarks for imaging and segmentation
• Data diversity scoring for demographics, pathology coverage, and imaging conditions
• Compliance-grade reporting for FDA and CE-style workflows

Fintech / Financial Services

Models

• Fraud/AML: XGBoost, LightGBM, LSTMs, GNNs, autoencoders
• Credit scoring: logistic regression, neural nets, boosting ensembles
• Algorithmic trading: DRL, LSTM, transformer time-series models
• Identity/KYC: FaceNet, ArcFace and liveness CNN pipelines
• LLM assistants for financial advisory and operations

Data Types

• Transaction tabular/time-series records
• Behavioral biometrics and device fingerprints
• Graph/entity relationship networks
• Face/KYC media, customer support text, market OHLCV streams
• Credit bureau and blockchain/ledger data

Adversarial Attacks

• Evasion and poisoning on fraud features and retraining flows
• Feature manipulation and inversion risks in credit systems
• Extraction attacks through API probing of trading models
• Deepfake and synthetic identity attacks on KYC
• Prompt injection and hallucination in financial LLM assistants

What You Gain With Robusto

• Tabular adversarial testing framework with poisoning detection
• Graph-based AML stress testing and adversarial node simulation
• Model extraction monitoring and API security diagnostics
• Regulatory-ready evidence packages (EU AI Act, DORA-oriented)
• Concept drift simulation for long-horizon model degradation

Robotics / Autonomous Systems

Models

• Perception: YOLO families, RT-DETR, Faster R-CNN, PointPillars, PointNet++
• Sensor fusion: BEVFusion, TransFusion, CenterFusion
• Navigation/planning: DQN, PPO, DDPG and related DRL methods
• VLA systems: RT-2, Octo, OpenVLA
• SLAM stacks including ORB-SLAM and LiDAR-first variants

Data Types

• RGB/stereo camera streams, depth maps and thermal frames
• LiDAR point clouds, radar and ultrasonic inputs
• GPS/GNSS coordinates, IMU and CAN bus signals
• Language instruction traces and manipulation sensor data

Adversarial Attacks

• Physical adversarial patches/textures and illumination attacks
• LiDAR spoofing and physical removal attacks in point cloud perception
• Cross-modal sensor fusion attacks on camera/LiDAR/radar consistency
• GPS spoofing, map poisoning, and timing-based navigation attacks
• CAN bus injection and actuator-level adversarial control perturbation

What You Gain With Robusto

• Physical attack simulation suite for camera and object pipelines
• LiDAR spoofing and point-cloud robustness toolkits
• Sensor fusion benchmark orchestration
• VLA-specific robustness testing (first-mover positioning)
• CARLA/SafeBench-style simulation integration for repeatable testing

Cybersecurity / AI Defense

Models

• Threat detection: XGBoost, LightGBM, random forest, and transformer-based anomaly detectors
• Email and phishing defense: BERT-style text classifiers, multimodal phishing detectors, and LLM triage workflows
• SOC copilots: retrieval-augmented LLM assistants for alert triage, incident response, and threat-hunting support
• Malware and binary analysis: CNN, GNN, and sequence models over binaries, API traces, and behavior graphs
• Identity and insider-risk systems: UEBA models, graph analytics, and behavioral anomaly detection

Data Types

• Network flow records, packet metadata, DNS logs, and endpoint telemetry
• SIEM events, authentication traces, and cloud audit logs
• Threat-intelligence text, vulnerability data, malware samples, and behavior graphs
• Emails, chat content, tickets, and analyst notes used in security copilots

Adversarial Attacks

• Prompt injection and retrieval manipulation against security copilots and analyst assistants
• Adversarial malware and phishing variants designed to evade learned detection features
• Poisoning of retraining loops, threat-intelligence feeds, and analyst feedback signals
• Alert flooding, feature camouflage, and concept drift exploitation in anomaly detection systems
• Model extraction and inversion risks when detection APIs or scoring services are externally exposed

What You Gain With Robusto

• Red-team evaluation workflows for security copilots before SOC rollout
• Adversarial testing for phishing, malware, and anomaly-detection pipelines
• Continuous validation across logs, graph, text, and multimodal security data
• Evidence packages that connect model behavior to operational risk and incident-response readiness
• Release-gate testing for security models as detection rules, prompts, and environments change

Solar / Energy / Smart Grid

Models

• Forecasting: ANN, CNN, LSTM, GRU, TCN, SVR, Gaussian-process variants
• Load forecasting: GRU-TCN with attention, denoising autoencoder stacks
• Grid optimization: DRL and generative scenario models
• Fault and anomaly detection: autoencoders, random forest ensembles
• TS-LLM pipelines in emerging forecasting use-cases

Data Types

• Weather and irradiance telemetry
• PV generation and load demand time-series
• SCADA/grid state measurements and smart meter streams
• Satellite cloud imagery and IoT sensor feeds

Adversarial Attacks

• R-FGSM style adversarial perturbations for regression forecasting
• False data injection in SCADA and smart-meter pathways
• Directionally targeted forecast manipulation
• Concept drift exploitation in long-lived models
• Anomaly masking attacks against fault-detection networks

What You Gain With Robusto

• Time-series adversarial library for forecasting/regression
• SCADA and smart-meter FDI simulation tools
• IoT integrity and poisoning diagnostics
• TS-LLM robustness evaluation modules
• Data diversity scoring for weather and generation edge coverage

Space Technology

Models

• Satellite imagery pipelines: ResNet/EfficientNet/YOLO variants
• Pose estimation and docking CNN stacks for GNC
• Telemetry anomaly detection with LSTM and autoencoder systems
• SDA object tracking/classification neural systems
• Edge-optimized onboard models (MobileNet/TinyML class)

Data Types

• Multispectral/hyperspectral/SAR imagery
• Onboard telemetry and orbital state time-series
• Star tracker and rendezvous camera feeds
• RF/communication and sensor fusion streams

Adversarial Attacks

• FGSM-like attacks on rendezvous and pose-estimation imagery
• Remote sensing perturbation and patch-based concealment
• Telemetry false data injection for failure masking
• Edge-model adversarial exploitation under compute constraints
• RF spoofing and orbital tracking manipulation scenarios

What You Gain With Robusto

• Edge-aware adversarial test harnesses
• Spacecraft GNC robustness toolkit
• Remote sensing adversarial benchmark sets
• Telemetry poisoning simulation and detection
• Digital twin integration for mission rehearsal scenarios

Surveillance / Security

Models

• Detection: YOLOv5-v9, Faster R-CNN, SSD, DETR class models
• Face systems: ArcFace, FaceNet, DeepFace
• Re-ID and tracking: OSNet, TransReID, DeepSORT, ByteTrack
• Video understanding: SlowFast, I3D, TimeSformer
• LPR and crowd analytics vision stacks

Data Types

• CCTV RGB/IR streams and facial media
• Body/gait features and trajectory metadata
• License plate frames and aerial/drone imagery
• Audio, depth/stereo and multi-camera metadata

Adversarial Attacks

• Physical adversarial patches and clothing-based evasion
• Adversarial accessories and deepfake spoofing for face systems
• Cross-camera transfer attacks for re-identification disruption
• Vehicle and LPR evasion via physical modifications
• Temporal perturbation attacks in video anomaly models

What You Gain With Robusto

• Physical patch/texture generation and red-team workflows
• Face verification robustness benchmarking across lighting/deepfake cases
• Cross-camera Re-ID resilience evaluation
• Video-temporal perturbation analysis
• Drone and remote-sensing adversarial benchmark modules