Overview of LightBeam’s AI Engine

AI in LightBeam powers accurate and scalable data security by extracting attributes from documents, tables, and images, classifying files by sensitivity and business context, detecting ransomware notes, and analyzing user behavior for anomalies. Customers can fine-tune sensitive-data templates inside their own environment to handle industry-specific patterns without exposing data externally. LightBeam’s architecture uses transformer-based NLP models, pattern-aware ML, behavioral analytics, OCR/vision models, and lightweight detectors.. All core models are pre-trained by LightBeam and delivered as packaged components, while any customer-specific tuning happens entirely within their infrastructure. No customer data is ever used for model training, ensuring strict privacy, compliance, and a zero-trust operating model.

AI-Powered Capabilities Within LightBeam

Attribute Extraction Across All Data Sources

LightBeam’s AI models extract rich contextual attributes, such as names, addresses, identification numbers, financial information, and health-related identifiers and more from documents, tables, and images. These attributes feed directly into the Data Identity Graph, enabling precise mapping between data elements and the individuals they relate to. This provides unmatched clarity into data lineage, residency, and exposure across structured and unstructured repositories.

Advanced Document Classification

Documents are classified by sensitivity, business purpose, compliance category, and domain context using transformer-based natural language models combined with OCR and multilingual pipelines. This hybrid pipeline allows LightBeam to interpret noisy PDFs, scanned images, and multi-format content with high fidelity. Classification models also detect ransomware notes and similar suspicious patterns early in the lifecycle, improving the speed and confidence of automated responses.

AI-Driven User and Entity Behavior Analytics (UEBA)

UEBA models create behavioral baselines for users, systems, and service accounts to detect anomalies and indicators of misuse. LightBeam identifies unusual access patterns, privilege escalation, abnormal file activity, and suspicious spikes in data movement. This context is correlated with data sensitivity and access posture, providing customers with risk-aware insights that go beyond traditional alerting.

Customer-Specific Templates and Fine-Tuning

LightBeam recognizes that each enterprise has unique data structures, industry terminology, compliance requirements, and document formats. To support this, the platform allows customers to build or tune templates entirely within their own infrastructure. These templates can capture industry-specific PII patterns, custom document types, and business terminology without exposing any data externally. Fine-tuning is fully private, executed inside the customer’s cloud tenant or on-prem environment , ensuring that sensitive content remains under customer control at all times.

AI Models That Power LightBeam

LightBeam uses a combination of specialized AI models optimized for accuracy, performance, and scalability:

• Transformer-Based NLP Models

Used for document classification, contextual sensitivity detection, and multilingual data interpretation.

• Pattern-Aware ML Models

Designed for structured attribute extraction, entity tagging, and domain-specific identification.

• Behavioral ML Models (UEBA)

Used to build user baselines, detect anomalies, and surface indicators of insider risk or misuse.

• Vision and OCR Models

Extract meaningful text and attributes from scanned PDFs, images, and low-quality documents.

• Custom Lightweight Detectors

Identify ransomware notes, suspicious markers, and file-based anomalies with minimal overhead.

LightBeam intentionally avoids training large language models on customer data. All LLMs and ML models are pre-trained on curated, compliant datasets and shipped as ready-to-use components.

A Zero-Trust Approach to Training and Data Handling

• LightBeam maintains an uncompromising position on data privacy and model training:

• All core models are pre-trained by LightBeam on controlled datasets.

• Customers receive a complete, optimized model package, including classifiers, F1-validated templates, and OCR/text processing pipelines.

• Any customer-specific model tuning happens within their cloud or on-prem environment, never leaving their network.

• LightBeam does not collect, store, or learn from customer data, no exceptions.

• No telemetry containing sensitive data is transmitted back to LightBeam.

• All AI operations comply with zero-trust security principles and enterprise governance requirements.

This ensures that organizations can deploy AI-powered data security while meeting strict privacy, regulatory, and risk management obligations.

A fully modular, AI-powered, cloud-neutral data security platform that runs entirely inside the customer's environment and unifies discovery, governance, security analytics, and privacy workflows through a single interface.