Generative AI (GenAI) represents a radical transformation in productivity. It enables immediate access to information, natural language data searching, and the simplification of tasks that previously took hours. However, for many executives and CISOs, this new reality comes hand-in-hand with new risks, vulnerabilities, and cybersecurity threats if certain precautions are not taken.
For many CFOs, the million-dollar question today is: how can we deploy AI without compromising the security, privacy, and compliance we already have in place?
The reality is that 57% of organizations estimate that their data is not ready for AI (Gartner, 2025 Hype Cycle for AI). When you introduce GenAI into a company, you are not just introducing a tool; you are introducing a new set of risks: authentication, versioning, content sprawl, data privacy, leak protection, regulatory jurisdiction, data sovereignty, retention, and more. Each of these is a potential risk vector if not managed correctly, which is why it is so important to have a data governance protocol.
This article lists the 8 specific challenges brought by GenAI, and how the right architecture (OpenText Extended ECM + Content Aviator) solves them.
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1. Authentication: Who has the right to process information with AI?
The challenge: when you introduce AI into your organization, a fundamental question arises: does a user's access to information in your system also grant them the right to have AI process that information? In many organizations, this is taken for granted. But in reality, it is a risk.
A user may have permission to read a confidential contract in their role as a lawyer, but should AI process that document, generalize it, summarize it, or expose it indirectly through generated responses? The answer is that it depends. It depends on explicit company policies, which often do not exist.
The solution: authentication integrated into the content management system.
OpenText Content Management (Extended ECM) integrates authentication and authorization at its core. When you use Content Aviator:
• Aviator inherits user credentials. In other words, there is no AI "super user."
• Access permissions apply equally to AI processing. Likewise, if you cannot read something yourself, the AI cannot process it either.
• Integrated SSO. A single authentication covers both document access and AI usage.
This eliminates a risk vector while simultaneously allowing the AI to automatically respect existing access policies.
2. Versioning: Is the user working with the correct information?
The challenge: Imagine a salesperson using AI to summarize contract terms with a client. But are they looking at the current version of the contract, or an outdated one? Without version control integrated into the content management system, it is impossible to know. The result ends up being decisions based on obsolete information, or even legal risk if the AI generates responses based on old versions.
The solution: version control integrated into OpenText Content Management (Extended ECM) that allows for automatic versioning:
• Every change to a document is recorded and the full history remains accessible.
• Content Aviator always accesses the current version and cannot work with outdated documents.
• Full traceability, including which version was used in each AI response.
Likewise, it ensures the creation of a corporate memory where the AI always works with verifiable and up-to-date information.
3. Managing Content Sprawl: Does GenAI contain or worsen content sprawl?
The challenge: content sprawl content sprawl is a problem that can encompass duplicate documents, unauthorized versions, and information in shared folders or ungoverned repositories.
When AI is introduced without a centralized foundation, the problem is amplified because unauthorized copies of documents are processed and responses are generated based on scattered, unverified information.
The solution: centralization and governance in OpenText Content Management (Extended ECM), which allows for the creation of a single source of truth. Furthermore:
• Content Aviator accesses only centralized content.
• Automatic retention, custody, and document deletion policies are applied uniformly.
4. Data Privacy: Is the AI processing personal information (PII) securely?
The challenge: data privacy is one of the biggest concerns with GenAI. What many teams are asking:
Can the AI process documents containing PII (names, addresses, bank details)?
Is it complying with GDPR, CCPA, and local regulations?
What happens if the AI exposes PII in a generated response?
Without explicit policies for data governance, these questions remain unanswered.
The solution: data governance integrated into OpenText Content Management Extended ECM + Content Aviator
• Automatic content classification - Detection of PII and sensitive data
• Access policies based on classification - Documents containing PII require additional approval for AI processing
• Anonymization or automatic masking - PII can be obscured before AI processes it
• Full traceability - Log of which sensitive data was processed, when, and by whom
5. Data Protection: Is there a risk of data leaks when AI processes and generates content?
The challenge: if AI processes confidential documents and summarizes them, generates content based on sensitive information, and allows users to download responses, all that content could leak and be exposed. Without data protection integrated into the AI lifecycle, every step is a risk vector.
The solution: end-to-end encryption + active protection
OpenText Content Management (Extended ECM) + Content Aviator implement three-layer data protection:
1. Data at rest: AES 256-bit encryption for documents, metadata, and vector databases
2. Data in motion: TLS/HTTPS for all communications (with LLMs, users, and APIs)
3. Active protection: Integration with Microsoft Purview Information Protection (AIP). This ensures that downloaded or emailed documents remain encrypted, requiring the user to authenticate every time they are opened.
Benefit: Even if data is leaked, it remains encrypted and unusable.
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6. Jurisdiction: Does AI usage comply with local regulations?
The challenge: operating across multiple countries, each with distinct regulations, many companies may be compliant in some territories but not others. Without granular control over where and how AI processes data, it is impossible to guarantee jurisdictional compliance.
For example, the GDPR in Europe requires explicit consent to process personal data, and data sovereignty laws in some countries prohibit specific data from being processed outside their territory. This is the case in Russia, where data on Russian citizens must be initially collected and stored on servers located within Russia before any transfer abroad; or Saudi Arabia, where the Personal Data Protection Law (PDPL) limits international transfers of personal data, allowing them only when specific legal and protection requirements are met.
The solution: Jurisdictional governance in Extended ECM
• Geographic classification - Documents tagged by country of origin.
• Processing policies by jurisdiction - Some data never leaves the territory; others require consent.
• Automated compliance reporting - Auditing which data was processed in which region.
• Integration with specific regulations - GDPR, ViDA, LGPD, PDPL, etc.
Benefit: AI can operate globally without violating local regulations.
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7. Sovereignty: Does AI usage violate data sovereignty policies?
The challenge: data sovereignty goes beyond jurisdiction. Some companies and governments have internal policies that prohibit corporate or classified data from being processed by external systems or in the public cloud.
For example, certain automotive and high-tech manufacturers often restrict the use of public AI for CAD drawings, technical specifications, or source code, preventing strategic information from leaving their corporate environments. In the financial sector (international banks), it is also common for entities to prohibit the entry of customer information, investment strategies, risk models, or regulatory documentation into public AI tools. In many cases, they only authorize AI solutions deployed in their own private cloud or a dedicated environment.
The solution: Ssecure LLM integration without data exposure
With Extended ECM + Content Aviator:
• Local grounding - RAG retrieves data from Extended ECM, not from external sources.
• Prompts without sensitive data - AI receives the question + necessary context, without exposing raw data.
• On-premises or private cloud LLMs - Option to deploy models internally.
• No retention in external LLMs - Data is never stored in OpenAI, Claude, etc.
Benefit: You can use AI without violating data sovereignty policies.
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8. Retention: Is AI still retaining data that should have already been deleted?
The challenge: data retention is also regulated. GDPR requires the deletion of personal data after consent is revoked. Tax regulations require invoices to be deleted after 7 years. Corporate policies require drafts to be deleted after 2 years.
But when AI processes that data to generate embeddings (numerical representations for search), are those embeddings deleted as well?
Without explicit governance, data that should be deleted remains accessible.
The solution: data lifecycle integrated into Extended ECM
• Automatic retention policies - By document type, jurisdiction, or user.
• Embedding deletion - When a document expires, its embeddings are also deleted.
• GDPR "right to be forgotten" compliance - Data and its derivatives are automatically deleted.
• Deletion audit - Report on what data was deleted and why.
Benefit: you meet data lifecycle requirements even with AI in the loop.
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How Extended ECM + Content Aviator solve all these challenges
The right architecture requires two layers working together:
OpenText Extended ECM (the centralizer):
• Content governance
• Access control and permissions
• Encryption and data protection
• Retention and lifecycle policies
• Audit and compliance
• Versioning and change control
OpenText Content Aviator (the AI agent):
• Accesses governed content in ECM.
• Inherits permissions, versions, and policies.
• Processes data securely.
• Generates verifiable and traceable responses.
• Complies with regulations without creating exceptions
The result: you can deploy AI without creating new silos, new risks, or new exceptions to your existing governance.
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Why Brait for your digital transformation
At Brait, we are experts in SAP integrations, such as OpenText Extended ECM + Content Aviator. We understand that these 8 challenges are not theoretical, but real risks that lead to fines, failed audits, and a loss of trust.
Our team, specializing in AI and regulatory compliance, works with your company to:
• Audit your current status - Identify specific risks in your current architecture.
• Design data governance - Explicit policies by content type, jurisdiction, and role.
• Integrate with your existing systems (SAP, CRM, etc.) or other OpenText solutions such as VIM (Vendor Invoice Management) or electronic invoicing systems in SAP.
• Train and scale - Organizational rollout without disruptions.
Contact us today for a no-obligation consultation!
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