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Unlock the Power of AI, Securely. Your Data, Your Control.


Today, mid-sized and large corporations are eager to harness the transformative power of AI, from legal contract classifiers to banking document retention analysers. However, a paramount concern for C-level leaders, CISOs, CIOs, auditors, and IT experts remains: "How is my data protected if we send it to an AI model?".


The "IseoMigrate" project, for instance, aims to leverage frontier models (LLMs) to populate a rich metadata database for huge file stores, assisting organisations migrate to new EDM platforms. We understand that sending proprietary documents and sensitive data to cloud AI services requires ironclad assurances. This page addresses your deepest concerns and demonstrates how secure AI adoption is not just possible—it's already happening.


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Your Data, Your Privacy: Uncompromised Confidentiality


The Concern: Companies worry that proprietary documents or personal data sent to a cloud AI API might leak or be used to train the provider’s model.


Our Assurance: Leading frontier LLM providers have implemented robust policies to prevent data leakage and unauthorised model training.


  • No Training on Your Data by Default: Major providers like OpenAI explicitly state they "do not train our models on your business data by default".

  • Data Stays Isolated: Microsoft’s Azure OpenAI Service guarantees that your prompts and completions "are NOT available to other customers" and "are NOT used to train, retrain, or improve" their AI models. Similarly, Google Cloud commits not to "use your data to train or fine-tune any AI/ML models without your prior permission".

  • Stateless Operations: In practice, these LLMs operate in a stateless, isolated fashion – meaning "no customer input is retained in the model’s memory or shared".

  • You Retain Ownership: You retain full ownership of your inputs and outputs. In short, your data stays your data.


Real-World Proof: Morgan Stanley, a global financial services leader, integrated GPT-4 only after OpenAI agreed to a "zero data retention" policy, ensuring none of the bank’s information is used outside their instance. This gave them the confidence that client data and intellectual property would remain confidential. Industry analysts confirm that "major players in the commercial LLM space have done a good job of offering contract terms that protect your inputs and outputs".



Navigating Compliance with Confidence: Structured Agreements and Standards


The Concern: Highly regulated sectors (such as finance, healthcare, and legal) must comply with stringent laws like GDPR, HIPAA, or banking regulations. Executives and auditors need to know that using an AI service will not violate these critical rules.

Our Assurance: Leading AI providers offer comprehensive support for regulatory compliance and legal agreements.

  • Structured Legal Agreements: Providers like OpenAI execute GDPR-compliant Data Processing Addendums (DPAs) with customers of their API and enterprise services. These agreements affirm that you control data retention and can set how long (if at all) data is stored on their systems.

  • Adherence to Industry Standards: Providers adhere to rigorous industry standards and undergo regular audits. For example, OpenAI has completed a SOC 2 audit and encrypts data at rest (AES-256) and in transit (TLS 1.2+), aligning with enterprise security requirements. AWS’s Bedrock service meets ISO, SOC 2, is HIPAA-eligible, and even FedRAMP High authorised for government use.

  • Data Residency: Concerns about data residency are also addressed. For instance, Microsoft’s Azure OpenAI processes and stores data within the customer’s chosen region or geography, helping to satisfy country-specific data localization laws.

These measures are meticulously codified in contracts and terms, providing enterprises with legal assurance that their data remains private, compliant, and under control.


Ironclad Security Controls: Operating Within Closed Boundaries


The Concern: IT and security teams demand assurance that there is no unauthorised access and that their data is isolated from the wider internet.


Our Assurance: Frontier LLM providers implement robust security measures and enable private, isolated deployments to ensure maximum protection.

  • End-to-End Encryption: Encryption is standard, meaning data is encrypted end-to-end (both in transit and at rest), so it remains protected even if intercepted.

  • Strict Access Controls: Enterprise offerings integrate with single sign-on (SAML SSO) and role-based access, ensuring that only authorised personnel can use or view AI outputs.

  • Network Isolation: Providers like AWS enable network isolation; with Amazon Bedrock, you can use AWS PrivateLink to keep all API traffic within your own private cloud (VPC), never exposing it to the public internet.

  • Private Model Copies: When fine-tuning an AI model on Bedrock, it is done on a private copy of the model, ensuring "your data is not shared with model providers, and is not used to improve the base models". This creates a closed-boundary approach that mirrors having an AI within your own secure environment.

  • Auditability and Monitoring: Vendors also provide audit logs and monitoring for governance. Amazon Bedrock offers CloudWatch and CloudTrail logs for all requests and responses, enabling you to audit how data is used and detect any anomalies. Content filtering and automated abuse detection are also in place to prevent misuse.


In summary, enterprise AI services are designed with a defense-in-depth security approach—from encryption and network isolation to monitoring—to satisfy even the strictest CISO or auditor.



Proven Success: Real-Life Stories of Enterprise AI Adoption

The most compelling evidence that secure AI adoption is not just theoretical but practical, is the growing list of industry leaders already using LLMs with sensitive data.

  • Financial Services: Morgan Stanley built an internal GPT-4-powered assistant on its proprietary knowledge base. Over 98% of its advisor teams now use it daily, confident that confidential client information stays in-house because OpenAI contractually ensured no data would leak or be used to train public models.

  • Legal Domain: Allen & Overy, a top global law firm, deployed a GPT-4-based legal AI called Harvey to over 3,500 lawyers across 43 offices. This was only possible because the platform was built to meet stringent client confidentiality standards, operating within A&O’s secure environment.

  • Pharmaceutical Industry: Daiichi Sankyo, a global pharmaceutical company, used Azure OpenAI to launch an in-house generative AI system ("DS-GAI") rapidly. Over half their employees are now using it daily for R&D and business tasks, safely utilising AI on proprietary scientific data due to Azure’s enterprise-grade privacy controls.

These stories confirm that even in highly regulated, security-conscious sectors, organisations are ready to trust LLMs with proprietary data because their AI solutions were implemented with clear privacy safeguards and trusted agreements in place.


The Bottom Line: Innovate with Confidence


Adopting AI doesn’t mean compromising on security. With the right provider assurances and controls – from contractual data protection commitments to encryption, isolated cloud instances, and compliance certifications – enterprises can confidently deploy AI-powered tools. The leading LLM providers (OpenAI, Microsoft Azure, Google Vertex AI, AWS, etc.) have set a high bar, ensuring that your data remains private, secure, and used only for your purposes. Armed with strict agreements and technical safeguards, even the most sensitive organisations are embracing AI innovation on their own terms. Secure AI adoption is not just possible – it’s already happening today, underpinned by robust privacy guarantees that eliminate the fear of data leakage.



For more in-depth information on the privacy and security commitments of leading AI providers, please refer to these resources:


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