September 2026 Summaries

AI governance is presented as a framework of policies, controls, and monitoring that helps organizations deploy AI securely, compliantly, and reliably while addressing risks such as data leakage, inconsistent definitions, weak access controls, limited auditability, and unreliable model outputs. Citing 2025 surveys reporting that privacy and security concerns caused many enterprises to abandon or delay AI initiatives, the discussion argues that governance is necessary to move projects beyond pilots and establish trust in production systems. A semantic layer is described as the operational foundation for this governance because it standardizes data definitions, applies access policies, preserves lineage, and supplies AI systems with contextual, compliant data that can reduce hallucinations. The CData Platform is positioned as a tool for creating this governed data layer across cloud, on-premises, and hybrid environments, offering controls for data residency, auditing, transparency, and fine-grained permissions. Its Talk-to-Your-Data interface is presented as an example of allowing employees to query governed enterprise data in natural language while applying security rules and maintaining traceability.

Unstructured data, which includes emails, social media posts, multimedia, documents, and IoT sensor output, represents an estimated 80–90% of business-generated information and lacks the predefined formats of traditional database tables. Its flexibility, diversity, and capacity for detailed contextual insights can help organizations identify customer sentiment, behavioral trends, operational patterns, and other information not readily visible in structured datasets. However, its volume, storage requirements, inconsistent formats, and need for preprocessing create scalability, management, and usability challenges. Semi-structured formats such as JSON and XML offer an intermediate level of organization, while tools including CData Sync, MongoDB, Microsoft Azure, Apache Hadoop, and Elasticsearch support the integration, storage, processing, search, and analysis of unstructured data. Techniques such as OCR, natural-language processing, transcription, machine learning, and distributed computing can help transform raw content into usable business intelligence.

Model Context Protocol (MCP) standardizes how AI applications connect to SAP data and actions, reducing the need for many point-to-point integrations by separating AI hosts and clients from MCP servers that expose SAP OData entity sets as controlled tools. An SAP MCP server can often be built with minimal or no custom code using Node.js and odata-mcp-proxy, with JSON configuration defining destinations, API paths, CSRF requirements, and entity sets that generate CRUD tools automatically. Secure deployment requires OAuth scopes, role-based access controls, tenant and audit alignment, and service-specific CSRF settings, while SAP BTP provides Destination, XSUAA, and Connectivity services for credentials, authentication, and on-premises access. Servers should be tested locally and in BTP, with MCP clients using supported transports such as stdio or Streamable HTTP to enable live SAP queries and multi-step workflows. MCP is most appropriate for real-time business questions, inventory or order checks, and governed cross-system agent workflows, whereas replication or semantic search may better suit static or batch-oriented data. Ongoing management includes consolidating related integrations where practical, maintaining simple version-controlled configurations, auditing permissions, and isolating failures across configuration, authentication, and client connection layers. Organizations needing less customization can instead use managed platforms such as CData Connect AI to provide centrally governed AI access to SAP data without operating the underlying MCP infrastructure.

CData’s Episode 11 of “Vibe Querying with MCP” demonstrates how its Google Drive MCP Server connects Claude to unstructured business documents, allowing users to retrieve, analyze, create, and update files through natural-language prompts. Using OAuth-based access and automatically discovered procedures such as ListFiles, GetDocumentContent, UploadFile, and UpdateResource, the setup avoids direct API knowledge, scripts, or manual file handling. The episode illustrates two workflows: converting a technical CData Sync whitepaper into an executive-ready summary saved as a new Google Drive document, and analyzing a sales-call transcript to identify customer pain points, priorities, recommended follow-up actions, and then append the analysis to the original file. The approach, described as “vibe querying,” is presented as a way to make document knowledge more accessible, reduce time spent on manual review, support iterative analysis, and help teams turn static files into interactive sources of business intelligence.

The Model Context Protocol (MCP) is an open-source standard that connects AI tools and agents with enterprise data, and Stripe offers two contrasting MCP approaches for accessing payment information. Stripe’s native MCP Server maps individual REST API endpoints to dedicated tools for actions such as listing customers, creating refunds, managing subscriptions, and searching documentation, providing a direct API-first model. CData’s MCP Server for Stripe instead presents live Stripe API data as relational tables, views, and procedures accessible through SQL, enabling filtering, joins, aggregation, and data modification through a smaller set of generic tools. While the native server may require agents to make multiple API calls, paginate results, and combine JSON responses client-side for complex analysis, CData can execute a single SQL query across customers, subscriptions, invoices, and line items to return aggregated results. The SQL-based approach is positioned as useful for AI-driven analytics, refund workflows, automated onboarding, cross-platform financial reporting, and revenue forecasting, while both options allow AI agents to access and act on Stripe data.

CData MCP Servers are Windows-based local services that wrap CData drivers to give AI clients such as Claude Desktop standardized Model Context Protocol access to enterprise data sources. They provide consistent tools for discovering schemas, tables, columns, procedures, and parameters, then querying data through SQL or executing source-specific stored procedures without custom integrations for each system. The servers dynamically retrieve metadata according to active user permissions, while optionally using caching to improve responsiveness, and translate diverse systems such as CRMs and ERPs into a common tabular SQL model. Performance features including query pushdown, parallel paging, and streaming support interactive workloads with large or complex datasets. Security is based on in-place data access, authenticated user permissions, centralized credential configuration, and support for OAuth SSO, Kerberos, and basic authentication, alongside SOC 2 Type II, ISO 27001, and GDPR compliance. CData initially offers the servers as a free beta for Windows and provides logging for administration and diagnostics, as well as an open-source read-only MCP server built on its JDBC drivers for developers to adapt.

“Vibe Querying with MCP” is a content series demonstrating how business users can use conversational AI to explore real-time sales and marketing data through CData MCP Servers connected to tools such as Salesforce, Google Analytics, and Claude. The first episode examines relationships between website activity and closed business deals, using natural-language questions to retrieve recent deal totals, isolate sales for a specific product line, compare new customers with expansions by industry, and connect Salesforce opportunity data with web-traffic behavior. The demonstration highlights cross-object Salesforce analysis and the ability to associate leads and contacts with Google Analytics metrics, producing findings about traffic channels, visitor engagement, conversion behavior, and differences between technology and professional-services prospects. It concludes by identifying the most visited URLs among high-value technology customers, presenting MCP-based querying as a faster alternative to traditional cross-platform reporting that often requires specialized data, marketing-operations, and technical expertise.

MCP (Model Context Protocol) servers are presented as standardized integration layers that connect AI applications with enterprise data, tools, and business workflows through secure, context-aware interfaces, commonly using JSON-RPC 2.0. Unlike traditional application-specific APIs, they aim to reduce custom integration work while supporting real-time data access, semantic metadata, centralized management, multi-AI compatibility, and governance features such as OAuth, role-based access controls, encryption, auditability, and network protections. An MCP ecosystem includes a host AI application, a client that translates requests, and a server that exposes approved enterprise resources; deployments can use frameworks such as FastMCP, Quarkus, Docker, or Cloudflare Workers depending on language, scale, latency, and hosting requirements. Recommended deployment practices include defining discovery, invocation, and status endpoints, choosing on-premises, cloud, containerized, or edge hosting, and maintaining centralized logging, health checks, metrics, alerts, and vulnerability monitoring. Common uses include AI-driven workflow automation, secure agent access to business tools, cloud API integration, and shared enterprise AI workflows, while CData Connect AI is promoted as a managed platform for connecting MCP servers to more than 350 data sources.

Model Context Protocol (MCP) is an open-source specification introduced by Anthropic that standardizes secure connections between AI applications, particularly large language models, and enterprise data sources, tools, and systems. Rather than improving a model’s inherent reasoning or creating business context itself, MCP provides a consistent runtime interface through which models can access authorized, relevant information such as customer records, policies, business rules, metadata, and system status. This can reduce the need for custom integrations, make context reusable across applications, and give data teams clearer control over what information is exposed and when. MCP can help make AI outputs more grounded in current business operations and support uses such as automation, reporting, and decision support, but it remains an early-stage standard and does not replace sound data architecture, governance, curated data, human oversight, or effective model development.

CData’s third Vibe Querying episode demonstrates how Senior Sales Director Jared Feeney uses the beta Salesforce MCP Server with Claude to analyze live Salesforce data through natural-language questions. Model Context Protocol connects AI clients to external data sources, and CData’s servers extend this access to more than 350 connectors, allowing sales leaders to explore deal portfolios without detailed schema knowledge or manually built reports. In the demonstration, Claude identified promising and at-risk North American opportunities, analyzed blockers using structured and unstructured Salesforce data, produced an individual deal-risk presentation, and created an interactive dashboard of U.S. closed-lost deals by product line. The post argues that these workflows can reduce report-building, research, and presentation preparation from hours or days to minutes while supporting forecasting, coaching, executive communication, and more timely decision-making. It also notes that insight quality depends on accurate Salesforce data, that the system can incorporate notes and emails beyond standard reporting fields, and that MCP servers can potentially write updates back to Salesforce.

AI systems that independently access enterprise sources such as CRM platforms, email, code repositories, and data warehouses require auditable records showing who queried what data, which systems were reached, what was returned, and when. The material argues that query-level logging at the data connectivity layer, rather than relying on model outputs or application logs, can support EU AI Act, SOC 2, ISO 27001, and GDPR-related compliance needs, particularly for high-risk AI systems. It recommends using OAuth and SAML passthrough authentication to link actions to real end users, workspace isolation to enforce least privilege, and SIEM exports combined with documented review and incident-response processes. It also emphasizes maintaining inventories of AI tools and approved data systems, retaining logs appropriately, capturing failed as well as successful requests, and monitoring for shadow AI, credential sprawl, and stale permissions. CData Connect AI is presented as a governed Model Context Protocol connectivity platform designed to centralize these controls by executing, attributing, logging, and exporting AI data-access events.

An anecdote about an AI-generated backpacking plan that understated route difficulty illustrates the risks of relying on models fed stale, incomplete, or disconnected information. The piece argues that as software companies add AI capabilities, trustworthy outputs depend less on the model itself than on access to accurate, current data from systems of record. It presents CData as an embedded data-connectivity provider used by major enterprise software companies, offering hundreds of maintained connectors to databases, SaaS platforms, ERP and CRM systems, and data warehouses. According to the company, outsourcing connectivity can reduce development and maintenance burdens, accelerate time to market, prevent data degradation caused by changing APIs, and enable AI features to provide more reliable answers.

MCP core maintainers’ August 22, 2026 roadmap identifies five future priorities with no stated release dates: agentic messaging, HTTP-native transport unification, agent identity and enterprise security, improved protocol primitives, and SDK developer experience. The analysis characterizes agent identity as the most urgent area because planned adoption of DPoP, workload identity federation, and token exchange would strengthen machine-to-machine authentication and reduce reliance on reusable bearer tokens, while noting that these measures address only the agent-to-server boundary. Enterprises must still independently manage credentials for downstream systems, enforce source-level user permissions, and maintain query audit logging. Transport work would extend Streamable HTTP to local environments and further replace deprecated SSE and stdio-based patterns, while improved primitives would standardize tool-result behavior and support progressive discovery for large tool catalogs. Agentic messaging is expected to introduce standardized server-initiated events, webhooks, and channels, whereas SDK improvements focus on documentation, ergonomics, and conformance testing. The roadmap follows features already shipped in the July 28, 2026 release, including stateless operation, discovery, caching, tasks, multi-round-trip requests, and enterprise authorization integration.

ChatGPT Enterprise connectors let users access external systems within existing permissions, with native integrations available for selected applications and custom Model Context Protocol connectors extending access to organization-specific tools and data. Secure deployment depends on governance measures including least-privilege access, per-user OAuth or SAML identity passthrough, source-system RBAC enforcement, narrowly scoped credentials, administrative controls over developer mode and write actions, and audit logging routed to SIEM platforms through tools such as the Compliance API. The guide argues that native connectors may be insufficient for data held in systems such as Salesforce, SAP, NetSuite, and ServiceNow, and presents CData Connect AI as a governed MCP layer that can provide centralized access across hundreds of sources while preserving user-level permissions and logging query activity. It also highlights risks including broad connector scopes, shared accounts, prompt injection, hallucinations, shadow IT, and permission creep, recommending testing, semantic data scoping, human approval for sensitive actions, and regular access reviews before scaling connected AI workflows.

Authorization is optional in Model Context Protocol (MCP), creating security risks when AI agents access enterprise data through broad permissions, shared credentials, or inadequately scoped tools. The guide argues that conventional access controls designed for human users are insufficient for agents, which can chain actions across systems and expose information through tool calls, APIs, or outputs. It recommends enforcing field-, row-, and table-level permissions at query time through identity passthrough, where source systems apply each requesting user’s existing OAuth or SAML entitlements rather than relying on service accounts or forwarding raw tokens, a practice MCP prohibits. Least-privilege controls should also isolate workspaces, limit available tools and operations, use short-lived scoped credentials, validate token audiences, and authorize every request independently. Detailed per-query audit logs linking user identity, actions, systems accessed, returned data, and timestamps are presented as important for incident response and compliance with ISO 27001, SOC 2, and GDPR. CData Connect AI is described as a platform implementing these practices through identity passthrough, workspace and toolkit boundaries, source-system enforcement, and audit logging.

Governed LLM data access ensures that AI models and agents retrieve enterprise information under each requesting user’s identity, enforce source-system permissions before retrieval, and create auditable records, reducing risks associated with shared service accounts, excessive access, data duplication, and compliance failures. The comparison evaluates 10 tools across identity passthrough, inherited role-based access control, audit logging, workspace isolation, and security certifications, grouping them into managed MCP platforms, AI gateways, integration platforms, and related identity, security, catalog, warehouse, and cloud services. CData Connect AI and Databricks Unity Gateway are presented as options emphasizing user-level permission inheritance and in-place querying, while Kong, MuleSoft, Merge, FastMCP, Okta, Wiz, Collibra, and Amazon Bedrock AgentCore address different aspects such as traffic policy, integration, framework development, identity issuance, monitoring, metadata governance, or cloud-native agent connectivity. The discussion advises organizations to verify OAuth and SSO behavior, source-level authorization, SIEM-ready audit trails, certification scope, and GDPR DPIA requirements, particularly because MCP itself does not independently enforce authorization and self-hosted solutions leave ongoing security and audit responsibilities with internal teams.

AI agents increasingly access enterprise systems and sensitive data, making them non-human identities that require unique credentials, least-privilege role-based access control (RBAC), traceable actions, and regular reviews rather than shared API keys. A recommended implementation process begins by inventorying all models, agents, data sources, and credentials; classifying data by sensitivity and regulatory requirements; defining minimal, time-bound roles; and enforcing permissions through a centralized AI gateway integrated with existing identity systems. Attribute-based access control (ABAC) can supplement roles by considering context such as device, time, location, and task, enabling just-in-time elevation for sensitive operations. Centralized logging, SIEM integration, anomaly detection, immutable retention, and periodic recertification support compliance, incident response, and ongoing governance, while common pitfalls include over-permissioning, incomplete audit trails, lack of tool-level controls, and neglected role reviews. CData Connect AI is presented as a managed gateway that applies identity passthrough, RBAC and ABAC policies, workspace and toolkit restrictions, and query-level auditing across enterprise data sources.

AI gateway performance should be evaluated by business outcomes rather than adoption or usage alone, using pre-deployment baselines and a three-layer framework covering infrastructure, data access, and organizational results. Infrastructure measures such as request volume, latency, token consumption, cost per request, cache performance, and provider availability reveal operational efficiency and potential issues with routing or data retrieval. Data access measures, including governed query volume, permission denials, audit-log completeness, schema exposure, and self-service rates, support compliance while helping diagnose whether failures arise from models, permissions, or underlying data. Business metrics such as time-to-answer, analyst hours recovered, first-contact resolution, cost per resolved inquiry, and attributable revenue connect AI investments to established financial measures, although returns may emerge over several years. A business case for a gateway can compare centralized integration with the costs and risks of custom connectors, account for token savings from schema-aware access, and estimate avoided governance costs. CData presents its Connect AI platform as providing built-in audit, token, access, and permission observability, and cites its own testing and benchmarks to support claims of improved token efficiency and grounded accuracy.

Model Context Protocol enables AI agents to connect with enterprise tools and live data but does not natively provide identity management, credential protection, access controls, or auditing, creating security barriers to enterprise adoption. The discussion identifies credential exposure, inadequate audit trails, permission drift, shadow AI, and prompt injection as central risks, citing research that found many MCP servers use hard-coded secrets and incidents involving malicious packages and destructive agent actions. It presents CData Connect AI as a managed MCP platform that addresses these gaps through identity passthrough from enterprise identity providers, OAuth 2.1 with PKCE, SSO-based authentication, per-request RBAC down-scoping, workspace isolation, and default-deny tool access. The platform also records attributed, query-level logs detailing users, queries, accessed systems, returned data, and timestamps, with SIEM export intended to support compliance and incident response. The proposed enterprise baseline emphasizes short-lived, user-bound authorization rather than shared accounts, centralized governance, and independently validated controls aligned with SOC 2 Type II, ISO/IEC 27001, GDPR, CCPA, and the Enterprise-Managed Authorization extension for MCP.

Enterprise AI delivers the greatest value when it can securely access and act on live business data rather than merely generate text, but adoption is often limited by data connectivity, context, and governance challenges. An AI gateway with a shared data layer can support natural-language business intelligence, conversational CRM and ERP copilots, cross-system enterprise search, regulated-industry applications, and automated actions in systems such as Salesforce, NetSuite, ServiceNow, and EHR platforms. Effective deployments require permission enforcement at the user, row, column, and record levels; comprehensive audit logging; schema-aware retrieval to limit irrelevant context and token costs; and real-time read and write access to source systems rather than stale warehouse copies. CData positions its Connect AI platform as an MCP-compliant enterprise data layer that connects AI tools and frameworks, including ChatGPT, Claude, LangChain, and CrewAI, to hundreds of cloud and on-premises sources while centralizing authentication, governance, auditing, and source-specific access controls.

A context layer is presented as infrastructure that supplies AI applications with live, accurate, permission-filtered data from enterprise systems such as CRM, ERP, HRIS, warehouses, and ticketing platforms, positioned between AI applications and underlying data sources. Unlike retrieval-augmented generation, which is suited to unstructured documents indexed periodically, a context layer is intended for current, structured operational data that may require cross-system joins, semantic interpretation, and transaction-level access controls; the two approaches are described as complementary in enterprise deployments. The proposed evaluation framework centers on source coverage, including custom fields and on-premises connectivity; schema intelligence and semantic resolution for identifying relevant fields, business definitions, and relationships; enforcement of each requester’s source-system permissions with auditable query logs; and measured outcomes such as grounded accuracy, token efficiency, and latency. The piece argues that enforcing permissions at the data source is essential for security and compliance, while noting that organizations should request benchmarks reflecting their own workloads. It concludes by describing CData Connect AI as a product designed around these capabilities and cites company testing and external research to illustrate the market’s growing focus on governed, hybrid retrieval for agentic AI.

AI agent data governance comprises the policies, technical controls, and monitoring practices that determine which enterprise data autonomous agents may access, what actions they can take, and how those actions are attributed to individual users. Unlike traditional governance focused largely on data access and movement, it must manage runtime behavior because agents can invoke tools, chain tasks, and potentially modify live systems, creating risks such as credential exposure, excessive privileges, incomplete audit trails, and uncontrolled expansion of access. Recommended foundations include source-level ownership, classification, lineage, RBAC, retention policies, and distinct agent identities, while four core controls are OAuth or SAML identity passthrough, permission enforcement under end-user roles, least-privilege workspace isolation, and detailed per-query logging exported to SIEM platforms. The approach aligns with frameworks including NIST AI RMF, SOC 2, ISO/IEC 42001, GDPR, and the EU AI Act, and emphasizes testing access boundaries, resource-bound tokens, token lifecycle management, and behavioral monitoring before moving pilots into production. It also argues that a centralized, platform-neutral connectivity and enforcement layer, such as CData Connect AI, can reduce duplicated governance work across multiple agent platforms while complementing their native security controls.

AI routing gateways effectively manage model selection, costs, rate limits, identity propagation, auditing, and request-layer security, but they cannot ensure that models receive accurate, current, or authorized enterprise data. The proposed context layer complements routing by connecting AI to live systems of record, applying user-level permissions before data reaches the model, and supplying semantic definitions that explain the business meaning of raw fields and relationships. Without this layer, models may rely on incomplete training data or stale retrieval indexes, resulting in hallucinations, incorrect interpretations, and possible unauthorized disclosures. Enterprise context consists of live operational data, shared semantic definitions, and harder-to-formalize tacit organizational knowledge, with the first two viewed as essential current capabilities. Effective AI infrastructure therefore requires both traffic governance through routing and data governance through a context engine, which can also reduce token costs by returning only relevant, permission-filtered fields and records. Organizations evaluating or building such a layer should consider live source access, delegated authorization, semantic resolution, source coverage, custom and on-premises system support, and detailed query logging.