Gencore AI for Slack Enterprise

Gencore AI helps organizations rapidly build secure, enterprise-ready generative AI systems, copilots, and AI agents. It speeds up GenAI adoption by simplifying the creation of structured and unstructured data pipelines that leverage proprietary enterprise data from hundreds of systems and applications.

Organizations can use 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 combining data from multiple sources. Built-in enterprise controls, AI usage monitoring, and end-to-end provenance tracking are enabled by default.

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

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

4. Protect AI Interactions
   A conversation-aware LLM Firewall safeguards user prompts, model responses, and data retrievals, helping prevent sensitive data leakage and defending against prompt injection, jailbreaking, and other attacks.

Key Features

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

2. Inline Security Across the AI Pipeline
   Enforce protection throughout the AI lifecycle with data sanitization before model consumption, policy-driven LLM firewalls, and continuous compliance monitoring aligned with frameworks such as NIST AI RMF and the EU AI Act.

3. Complete AI System Visibility and Provenance
   Gain full transparency into data usage and AI activity—down to individual files, users, models, and inference endpoints.

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

How to load Slack Enterprise data in minutes for your GenAI pipeline

1. Select Slack Enterprise as the data source.

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

3. Define the scope for Slack Enterprise data.

4. Optionally apply filters such as:

   • Object prefix (text or regex)

   • Object name (regex-based)

   • Object tags (key:value)

   • Extension category

   • File extensions

   • Object size

   • Last modified date

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