For leaders in regulated industries looking to take their organization's knowledge management solutions to the next level, the question is no longer "Should we deploy AI-powered enterprise search solutions," but "How do we deploy them securely and compliantly?"
Some organizations are comfortable handling sensitive financial, client, or proprietary data within a multi-tenant cloud infrastructure. Others, meanwhile, continue to be wary of letting any sensitive data leave their own security perimeter. As a result, the conversation surrounding enterprise-grade GenAI, and more specifically, AI-powered enterprise search solutions, has settled around a spectrum of architectures designed for control and compliance.
The two primary models of choice for secure AI enterprise search solution deployments are the Virtual Private Cloud (VPC) and the on-premise data center. Choosing between the available deployment models for enterprise GenAI platforms depends on a variety of regulatory, client, and internal risk management drivers.
This guide explores this spectrum of secure options, helping you understand the drivers for each and highlighting why finding a vendor that delivers both is a significant strategic advantage.
Understanding VPC and On-Premise
Your organization's security and compliance needs dictate your AI architecture, not the other way around. A one-size-fits-all approach is not an option when choosing between technically sound solutions for secure cloud and on-premise environments.
Virtual Private Cloud (VPC): The Standard for Secure Enterprise AI
For many of the world's leading financial institutions and regulated companies, a VPC has become the deployment model of choice. By creating a logically isolated section within a public cloud, a VPC provides a secure, single-tenant environment that offers significant control over network and data access. This architecture, which we’ve implemented in some of the most demanding industries, e.g., financial regulation, provides a powerful combination of enterprise security and managed infrastructure.
On-Premise: The Solution for Specific Mandates
For some organizations with specific requirements for data residency and control, on-premises enterprise search solutions remain the only viable path to AI adoption. In this architecture, all software, data, and processing reside within the organization's own data centers, satisfying non-negotiable policies that require absolute data sovereignty and physical control over the infrastructure. The challenge for many companies is that very few enterprise AI vendors cater to this deployment option.
When is On-Premise a Requirement?
The decision to demand an on-premise deployment is a strategic one, often driven by factors that go beyond technical security specifications. The most common drivers include:
- Absolute Data Sovereignty: Certain national or industry regulations leave no ambiguity, requiring sensitive data to remain physically within a country's borders or a company's own walls.
- Intransigent Stakeholder Policy: A company's board, CISO, or key clients may enforce a strict policy against having their most valuable data reside on any third-party infrastructure.
- Eliminating Third-Party Risk: For the most risk-averse organizations, on-premise deployment removes the entire category of risk associated with a cloud provider's personnel, policies, or potential security breaches.
Foundational Capabilities for Any Secure Enterprise Search Platform
Whether deployed in a VPC or on-premise (or, for that matter, in a more complex hybrid scenario), a powerful and reliable AI-powered enterprise search platform can leverage a core set of value-adding capabilities:
- Traceable, Accurate Results: Using enhanced retrieval augmented generation (RAG) to ensure every AI-generated answer is grounded in and cited from your specific enterprise documents.
- Deep Connectivity: Building an enterprise taxonomy and a semantic knowledge graph from all your disparate data sources to deliver rich, contextual insights.
- Granular Security: The ability to inherit and rigorously enforce your existing access control lists (ACLs) is vital, ensuring users only find information they are already permitted to see.
- AI Guardrails: Implementing robust guardrails to prevent off-topic or inappropriate AI responses, ensuring all interactions are safe, compliant, and aligned with your corporate policies.
- Data Virtualization: Connecting to and querying real-time operational data directly where it resides, without the cost and complexity of moving or replicating it into a separate data store.
- PII Masking: Automatically detecting and redacting sensitive personally identifiable information (PII) from documents and results to ensure data privacy and regulatory compliance.
Squirro: Expertise Across the Full Deployment Spectrum
At Squirro, our philosophy has been to provide clients with the architectural flexibility to meet their unique security requirements. We have garnered deep experience deploying our secure, feature-rich enterprise GenAI platform within the VPCs of top-tier global banks and financial institutions.
But, we are also among a very select group of vendors to deliver our platform in a full on-premise deployment. Our adaptable, security-first architecture lets us deliver transformative AI, regardless of your infrastructure requirements.
Get Started Today with Secure Enterprise AI
Your journey into generative AI requires a partner who understands that security and flexibility are not mutually exclusive. A successful deployment is one that aligns with the specific compliance, risk, and stakeholder realities of your organization.
To explore the technical underpinnings of a secure GenAI deployment in greater detail, we invite you to download our guide.
Download our Technical Essentials Guide: Transform Banking & Financial Services with Enterprise GenAI.
This guide offers a deep dive into the practical strategies for implementing secure and effective AI, covering:
- Deploying GenAI while ensuring PII protection and data security compliance.
- The essential building blocks of a reliable platform, like Enhanced RAG and AI Guardrails.
- Actionable use cases for automating tasks and enhancing decision-making in the BFSI sector.
Download the guide now to build a secure and powerful strategy for generative AI.
