Unlocking Enterprise AI: Safe, Secure, and Scalable

In an era where enterprises are harnessing the power of generative AI to drive productivity, one core challenge remains: safely integrating data across diverse systems while maintaining robust security and governance. As a Securiti Partner, I see firsthand how advancements like Gencore AI address this challenge, creating pathways for enterprises to embrace AI responsibly. This technology provides an infrastructure that enables organizations to safely leverage data for enterprise-grade AI, offering the essential tools to accelerate secure, effective generative AI development.

Knowledge Graph for Enhanced Data Control

Central to this platform is its comprehensive knowledge graph, a breakthrough that maps and categorizes data across structured and unstructured systems—whether on-premises, in the cloud, or within SaaS environments, maintaining granular contextual insights about the data and AI systems. Native connectors facilitate scanning, tagging, and classifying data from diverse sources, creating a holistic view of sensitive data across an organization’s infrastructure. This knowledge graph enables robust governance, maintaining precise information about access entitlements, regulatory compliance, and AI system lineage, thereby easing privacy management, sensitive information access, and data compliance across AI applications.

Data Preparation and Compliance in the AI Pipeline

This platform integrates seamlessly with various Large Language Models (LLMs) and vector databases, allowing enterprises to curate and process data at scale for optimal AI output. By transforming structured and unstructured data into embeddings and syncing it with vector databases, this solution streamlines AI processing while ensuring security measures are in place. Automated redaction capabilities mask sensitive data, such as personal information or proprietary assets, before it enters the AI pipeline. Compliance features reflect current GDPR, HIPAA, EU AI Act, and other regulatory standards, creating a safe and adaptable solution for enterprises with strict data requirements.

A Three-Pronged Firewall System for AI Security

One standout feature of this technology is the three-pronged firewall system designed to address the unique security challenges in using enterprise AI. Enterprise AI systems face significant vulnerabilities, such as data leakage, malicious prompt injections, and unauthorized data access. This platform counters these risks through a contextual firewall approach, which operates at three critical junctures: prompt, retrieval, and response.

1. Prompt Firewall: This layer prevents malicious prompts from reaching the AI model, harnessing a wide range of embedded and custom policies.
2. Retrieval Firewall: Here, access rights are checked in real-time, ensuring users retrieve only data they’re entitled to. Two users asking similar questions may receive different responses based on their access levels, safeguarding against unintended breaches. Additional policies are enforced to protect against sensitive data exposure and ensuring retrieved data aligns with topic criteria.
3. Response Firewall: The final layer ensures that responses align with corporate policies and regulatory requirements, maintaining compliance and security and ensuring appropriate responses (e.g., mitigating toxicity).

Together, these three firewalls protect against data leakage, sensitive data exposure, and other AI-specific security risks as well as ensure adherence to corporate policies.

Real-Time Entitlement Checks and Access Rights

Gencore AI’s knowledge graph is instrumental in maintaining security through real-time entitlement checks, which apply to every user interaction with the AI. This feature ensures that data access is consistent with each user’s role, providing separate access levels for users in different departments—such as HR versus engineering. By maintaining these checks, the platform prevents unauthorized data use, upholding data governance principles throughout the AI process. In addition to robust security, the platform offers flexibility in selecting LLMs and vector databases, allowing users to optimize for cost, compliance, and performance while also enabling users to configure AI systems without extensive technical expertise.

End-to-End Traceability and Observability

Another unique feature of this technology is its end-to-end traceability and lineage tracking, providing real-time visibility into the entire data pipeline. From individual files to users, vector databases and AI models, this traceability ensures comprehensive observability of how data moves within the AI system. The knowledge graph continually updates itself, maintaining current insights into data location, access rights, and compliance. This 360-degree view is invaluable for organizations looking to scale AI responsibly, with full visibility into data flow and security.

The Future of Secure Enterprise AI

With its integrated data controls, compliance tools, and model flexibility, this platform enables enterprises to unlock the full potential of their proprietary data while meeting strict privacy and security standards. For organizations that seek the power of AI without compromising governance, this platform simplifies the creation of enterprise-grade, secure AI, bridging the gap between innovation and responsible data management.

For more information on harnessing the power of generative AI while ensuring data integration security, visit https://bit.ly/4g2qAD1.