Scaling AI Securely: Key Privacy and Governance Challenges Explained

Scaling AI Securely: Key Privacy and Governance Challenges Explained


We all need to innovate, but we can't afford to compromise on security or regulatory adherence. For those in the banking, financial services, and insurance (BFSI) sector, where business builds on handling personal data, or in manufacturing, where intellectual property is king, deploying generative AI can feel like a tightrope walk.

The same logic applies to privacy at scale; if a project only involves basic company files, the private handling of vectors at scale is not yet a pressing issue. Why? Because test data is rarely as sensitive as the sensitive data you operate on daily.

This leads to a familiar picture: We’ve repeatedly seen organizations mistake pilot success for deployment readiness, only to be blindsided by the complexities of scaling generative AI.


Addressing Generative AI Privacy Challenges


Generative AI privacy concerns can been addressed. If you aren’t convinced, consider this: For years, Squirro  scalable enterprise-grade GenAI platform has been relied on by central banks, financial regulators, and some of the leading financial service providers


Can GenAI drive value and preserve privacy?
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One of the first hurdles that companies encounter is data residency. Dealing with sensitive first-party data – investment research, customer details, proprietary designs – information that simply cannot leave your sphere of control, often means opting for deployment options within Virtual Private Clouds (VPCs) or on-premises environments. It's about keeping your data where it belongs: with you.

Another key consideration is whether the GenAI runs within your own tech stack or in a multi-tenant environment. Hosting it in your Virtual Private Cloud (VPC) or on-premises keeps data under your control, while multi-tenant solutions introduce risks by processing data in shared environments, often with limited visibility into where sensitive information may travel.

But data residency is just the beginning. Consider the journey of a query: data from your knowledge base is pulled and then fed to a large language model (LLM) to generate a response. If those LLMs aren't under your direct control, personally identifiable information to third-party providers can be exposed potentially. (Examples of PII include personal details like names, email addresses, account and Social Security numbers, etc.) This is a red flag, especially when dealing with AI privacy laws and strict regulations such as GDPR, HIPAA, CCPA/CPRA, or PDPA.  


What can you do? Recommendations from our Chief Product Officer


Some best practices that can help you navigate these challenges:

  • Enterprise LLMs: Opt for self-hosted or private LLM instances where you have complete control over the data flow. This provides the highest level of security. 
  • Data Processing Agreements (DPAs): If you must use external LLM providers, ensure robust DPAs are in place. These contracts should explicitly outline data handling and compliance commitments.
  • Data Anonymization: Where possible, strip out the PII. Anonymize or pseudonymize the data before it ever reaches the LLM. This is a strong first line of defense.
  • Access Controls: Implement strict access controls. Only those who absolutely need to should be able to input or retrieve sensitive information – like your clients’ PII – from the system.
  • Audit and Monitoring: Regularly review logs and policies. Continuous monitoring is essential to catch any potential compliance breaches.


What are the key differentiators among AI vendors concerning privacy, compliance, governance, and control?


A comprehensive guide for evaluating Enterprise GenAI Vendors
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Innovation but not at the expense of governance or privacy. Squirro platform integrates add-on modules like our privacy layer. It acts as an intermediary, cleansing data of PII before it interacts with the LLM. When a prompt comes in containing PII, the system hashes the PII so that it is masked to the LLM. Next, the LLM generates a response, and then the original PII is securely re-integrated into the final output. It's about having the right controls in place, not just asserting access rights. While PII sensitivity is key, it’s almost more critical to ensure that, within the organization, the right data doesn’t get to the wrong person.


While many genAI platforms or internally built solutions can easily ensure the right data gets returned, it’s much harder, and more important, to ensure that the right data goes to the right personAccess control, or data governance, is paramount to preventing data leaks, both internally and externally. 

I am thankful to key customers in the regulatory space who have pushed us to the limits to ensure that, when we say we can do privacy and governance at scale, we actually deliver. - David Hannibal, CPO & Head of Corporate Development at Squirro


Squirro: Leading the Way in AI Scaling


Why Squirro Is Trusted by
Regulated Industries over SaaS GenAI
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Granted, with some resources, most engineers could build a RAG for 100 users across thousands of documents fairly easily. But, the real challenge – and where Squirro truly shines – lies in managing privacy, i.e. a company’s data governance rules, and optimizing costs at this scale. 


  • 10,000 user deployments at a single enterprise? Check.
  • 10,000+ user group privacy configurations? Check.
  • Multi-system governance unification in regulated and government agencies? Check.
  • 15 million+ document deployments with an average PDF length exceeding 250 pages? Check.


Privacy at scale matters as organizations deploy generative AI across their operations. At Squirro, we have the expertise and proven track record to navigate this landscape. We’ve earned the trust of regulators, governments, Tier 1 banks, and some of the largest organizations worldwide. For those looking to deliver AI scalability while adhering to governance layers, we are here to discuss the journey we’ve undertaken. 



Upcoming Webinar: Scaling GenAI for Maximum Impact From Pilot to Enterprise-Wide Deployment

Many organizations begin their GenAI journey with promising pilot projects, but they often struggle to scale AI across the entire enterprise. Scaling GenAI involves more than just technology; it encompasses strategy, security, and impact. In this context, we will explore how to redefine enterprise AI using Knowledge Graphs (KG) and advanced security and privacy features. This approach ensures that organizations can scale AI with confidence.


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Join our webinar featuring Gartner expert Darin Stewart (VP Analyst at Gartner), along with Dorian Selz (CEO & Co-Founder at Squirro) and David Hannibal (CPO & Head of Corporate Development at Squirro) , to uncover critical lessons learned from GenAI pilots and gain a roadmap for seamless enterprise deployment.

Register here to access the webinar or watch the replay on demand: https://squirro.com/webinar-scaling-genai-for-maximum-impact



Recognized by Gartner as a visionary company, Squirro stands at the forefront as an enterprise-ready generative AI solution for search, insights, and automation. Our clientele includes prestigious organizations such as:  the European Central Bank, the Bank of England, Henkel, Mubadala.

Thank you for being part of our journey. Stay tuned for more updates as we continue to bridge the AI reality gap!


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