--- author: OOMOL author_url: https://oomol.com/about/ datePublished: 2026-10-01 title: "The better AI knows you, the more it can help: giving agents context safely" description: Rich personal and business context helps agents offer more useful advice and services. Learn what information they need and how OOMOL approaches data protection, identity, compliance, and open source. lang: en canonical_url: https://oomol.com/blog/agent-data-security/ markdown_url: https://oomol.com/blog/agent-data-security.md --- ![An AI assistant connects personal and business data, with external protection and identity-based internal access controls](/blog/agent-data-security/en-cover.webp) **The better an agent knows you, the more useful its advice and services become—and the smarter it can seem in everyday work.** From Meta’s Muse to OpenAI’s dot, AI assistants are starting to remember preferences, connect apps, and work on ongoing tasks. [Meta’s introduction to Muse](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/) · [OpenAI’s introduction to dots](https://openai.com/index/introducing-dots/) Even a powerful model needs to know your goals, circumstances, and constraints. A personal assistant needs context from calendars, email, conversations, and preferences. A business assistant needs information from ERP, CRM, and office automation (OA) systems, alongside external market information, to advise like a consultant who understands the business or act within its authorization. Access to that information is a prerequisite for useful AI adoption. How we protect it determines whether we can trust an agent with our work. ## Enough context helps an agent think things through An assistant that knows your destination can recommend hotels. One that also knows your meeting time, the customer’s address, your calendar, and travel preferences can choose a suitable flight, spot scheduling conflicts, and remind you to prepare customer materials. It considers more because it knows more about your situation. Business questions are more complex. An operations lead asks: “Which products should we focus on promoting next month?” The agent needs customer demand from the CRM, stock and delivery capacity from the ERP, approval requirements from the OA system, plus sales, payment collection, and market changes. Looking only at demand might lead it to recommend a product the company cannot supply. Looking only at stock might overlook a new market opportunity. Looking only at sales might miss pressure on margins and payments. **An agent needs details and a broad view of the business.** It must understand the relationships between customers, supply, finance, and the market to judge whether advice fits this company. Sufficient context includes accurate, current business data, historical background, organizational goals, and decision constraints. Together, they help the agent understand the business so it can put the model’s reasoning abilities to use. ```text Goals, preferences, history, and business constraints + ERP: stock and delivery · CRM: demand · OA: approvals + Industry trends and market information | Access authorized by identity and task ↓ Agent connects information and understands the business ↓ Better-grounded advice, more thoughtful services and actions ``` *Diagram: how an agent uses context to provide services. Model capabilities and data quality both affect its judgment; people still need to review consequential decisions.* ## What happens if a business does not adopt AI? One company uses agents to continually summarize customer feedback, check stock, and track market changes. Another relies on employees to look through each system, assemble reports, and pass information along. As the first makes this work faster and more reliable, the gap in response times and coordination costs grows. If competitors keep improving efficiency while a company sticks with its existing methods, keeping up becomes harder. The accumulated gap in customer experience, operating costs, and decision speed can erode its competitiveness and eventually push it out of the market. Businesses need to adopt AI and address the security issues that come with data access. They can start with a concrete task—preparing for a customer conversation, investigating a stock anomaly, or producing a market brief—give the agent the information it needs, and expand use based on results. ## External security: protecting data and credentials Once an agent connects to business systems, security must cover both external protection and internal permissions. External protection starts with preventing unauthorized access. Business data and credentials such as OAuth tokens and API keys both need protection. Leaked credentials can give an attacker continued access to the original system. Data also needs careful handling once it enters model context, task records, or logs. OOMOL’s cloud uses **envelope encryption** to protect sensitive data: a data encryption key encrypts the data, and another key protects that data encryption key. App calls operate within authorized scopes, without exposing account passwords or raw tokens to members. [Security and compliance](/security/) Businesses also need to establish which models and services will process the data, how long it will be retained, and how authorization can be revoked. These arrangements determine how data remains protected after it leaves the original system. ## Internal security: permissions must follow identity Data can leak even when it stays inside the company. A salesperson’s access to their customers does not grant access to everyone’s salaries. An operations employee’s ability to query stock does not grant permission to change purchasing approvals. Financial information used by a manager must not become available to other members simply because it entered a shared assistant’s memory. **Who can read which data and perform which actions must be tied to identity and checked by the system.** Access checks should happen before data reaches the model. OOMOL administrators can configure the operations allowed for each connection and specify which members may use it. The same app account can have multiple connections with different operation scopes for different members. App authorization and the caller’s identity further constrain the resulting permissions. [Access control](/docs/access-control/) The connection layer controls access to connections and operations. Access to a particular customer’s or department’s records also depends on the original system’s permissions and the application implementation. Assistant products and workflows need controls for shared memory and result recipients, too. Permission to read a financial report, for example, does not automatically authorize an agent to send it to an all-company channel. ## Compliance: security assessments and personal data protection OOMOL has passed **TAC Security’s CASA/ESOF security assessment** and follows **GDPR requirements for personal data protection**. Its privacy policy explains data processing, user rights, and how to submit privacy requests. [OOMOL security and compliance](/security/) · [Privacy policy](/privacy/) CASA/ESOF provides application security assessments within the relevant scope. GDPR sets requirements for processing personal data, including purpose, data minimization, and individual rights. [TAC Security assessment information](https://tacsecurity.com/esof-appsec-ada-casa-faqs-2/) · [EDPB data protection guidance](https://www.edpb.europa.eu/sme/be-compliant/be-compliant_en) These requirements need to shape development and operations: define data use and retention, process only what is needed, and let users manage authorization and submit privacy requests. Businesses selecting a service can use them to examine assessment scope, data flows, and each party’s responsibilities. ## Open-source review: making key implementations inspectable OOMOL maintains the open-source connection gateway **OpenConnector** and publishes the source for **oo CLI** and **Connector SDK**. OpenConnector uses the Apache 2.0 license; the public oo CLI and Connector SDK repositories use MIT. Developers can inspect how calls are sent, how connections execute them, and where permissions are checked. They can report issues or help fix them. Open source gives the community an opportunity to review the implementation and lets businesses inspect key code directly. Whether a deployment matches that code and whether its configuration meets security requirements still need to be checked. We want users to see key implementations and follow issues and fixes, building trust through that transparency. [OpenConnector source](https://github.com/oomol-lab/open-connector) · [oo CLI source](https://github.com/oomol-lab/oo-cli) · [Connector SDK source](https://github.com/oomol-lab/connector-sdk) ## Start with one task Choose a task you want an agent to take on. List the information it needs, the operations it may perform, and who may see the results. Connect the relevant systems, configure permissions, and check whether its work is useful. As the task develops, add missing background and information from other systems while reviewing new data access and operation permissions. OOMOL supports this process through encryption, access control, security assessments, and open-source components, helping individuals and businesses use agents with greater confidence. [Learn about OOMOL security and compliance](/security/) · [Configure connection permissions](/docs/access-control/)