Gencore AI for Confluence Cloud

Gencore AI helps organizations rapidly and easily create secure, enterprise-ready generative AI (GenAI) systems, copilots, and AI agents. It accelerates enterprise GenAI adoption by simplifying the creation of AI and data 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 leading 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 unifying data from multiple sources. Built-in enterprise controls, AI usage monitoring, and end-to-end provenance tracking are automatically applied.

2. Securely Sync Data to Vector Databases
   Ingest and synchronize data at scale from diverse systems. Generate custom embeddings with rich metadata to prepare enterprise data for LLM-powered use cases.

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

4. Protect AI Interactions
   A context-aware LLM Firewall safeguards prompts, responses, and data retrievals, ensuring interactions comply with enterprise policies and defending against threats such as prompt injection, data leakage, and jailbreaking.

Key Features:

1. Enterprise-Wide Data Connectivity
   Securely ingest data through hundreds of native connectors, enabling AI applications that span unstructured and structured data across SaaS platforms, on-premises systems, public clouds, and data clouds.

2. Inline Security Across the AI Pipeline
   Protect every stage of AI operations with layered security, including data sanitization prior to model usage, LLM firewalls for policy enforcement, and continuous compliance monitoring aligned with standards such as NIST AI RMF and the EU AI Act.

3. Complete AI System Visibility
   Achieve full transparency across your AI ecosystem, with detailed insight into data usage, users, files, models, and AI endpoints.

How to load your Confluence Cloud data in minutes to build a GenAI pipeline:

1. Choose Confluence Cloud as the data source.

2. Select the specific data system, bucket, or relevant attributes.

3. Define the scope applicable to Confluence Cloud.

4. Optionally apply filters such as:

   • Object prefix (text or regex)

   • Object name (regex-based)

   • Object tags (key:value pairs)

   • Extension category or specific extensions

   • Object size

   • Last modified date

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