Gencore AI for NFS

Gencore AI helps enterprises rapidly build secure, enterprise-ready generative AI (GenAI) systems, copilots, and AI agents. It accelerates GenAI adoption by simplifying the creation of AI pipelines that combine structured and unstructured proprietary data from hundreds of enterprise data sources and applications.

Organizations can leverage any foundation model available on Google Cloud—such as Vertex AI, Gemini, and PaLM 2—as well as third-party models including Anthropic Claude, Meta Llama 2, and Mistral.

Key Capabilities

1. Build Secure Enterprise AI Copilots
Quickly create AI copilots and knowledge systems by merging data from multiple sources in minutes. Built-in enterprise controls provide AI usage monitoring, governance, and full data provenance.

2. Safely Sync Data to Vector Databases
Securely ingest and synchronize data at scale from diverse systems. Generate custom embeddings enriched with metadata to prepare enterprise data for LLM-powered applications.

3. Curate and Sanitize Data for Model Training
Easily assemble, clean, and sanitize high-quality datasets to support AI model training and fine-tuning.

4. Protect AI Interactions
A conversation-aware LLM Firewall safeguards user prompts, model responses, and data retrievals to ensure secure and policy-compliant AI usage.

Key Features

1. Seamless Enterprise Data Connectivity
Ingest data securely through hundreds of native connectors, enabling AI applications that span SaaS platforms, on-premises systems, public clouds, and data clouds.

2. Inline Security Across the AI Pipeline
Protect AI workflows with layered security controls, including data sanitization before model consumption, LLM firewalls, and continuous compliance monitoring aligned with standards such as NIST AI RMF and the EU AI Act.

3. End-to-End AI System Provenance
Gain complete visibility into AI systems, tracking data usage, users, files, models, and endpoints with granular transparency.

For support, please visit: https://securiti.ai/contact-us/

How to Load NFS Data to Build Your GenAI Pipeline

1. Select NFS as the data source.

2. Choose the relevant data system, bucket, or attribute.

3. Define the scope for the NFS data.

4. Apply optional filters such as:

   • Object prefix (text or regex)

   • Object name (regex-based)

   • Object tags (key:value pairs)

   • Extension category

   • File extensions

   • Object size

   • Last modified date

5. Click Save and Continue to proceed to the Data Sanitizer stage.