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Use AI as an accelerator, not as a security authority. Let it draft screens, workflows, tests and code, but treat every generated artifact and configuration as untrusted until a person reviews, tests and approves it. Start by defining the app’s data and security boundary, give the model only the context it needs, enforce least-privilege access at the data source, constrain connectors and write actions, and keep monitoring after launch.
What makes an AI-built internal app risky?
An internal app can expose payroll records, customer information, intellectual property or operational systems just as seriously as a public application. The fact that it is behind a company sign-in, made with a low-code platform or generated by an enterprise-approved model does not make it safe by default.
AI introduces additional paths for failure:
- Generated code and configuration may contain vulnerabilities. A model can produce an insecure authorization check, an over-broad database query, an unsafe file-upload path or a connector configured with excessive rights.
- Prompts and retrieved context may contain sensitive data. Credentials, tokens, personal information and confidential documents can be copied into a prompt, retained under a provider’s terms or exposed to a user who should not see them.
- Model output can be manipulated. Untrusted text in a document, email or web page can attempt prompt injection and influence an agent or workflow.
- Automation can turn a mistake into an action. An assistant that can send messages, change records, transfer files or modify configuration needs stricter controls than a read-only chatbot.
- Low-code abstractions can hide important settings. A prebuilt connector may simplify development while leaving identity, sharing, environment, network and logging decisions to the builder.
NIST’s AI-specific secure software guidance says to “Code the handling of inputs (including prompts and user data) and outputs carefully.” That means AI-generated code receives the same secure-development review as hand-written code, with extra scrutiny around prompts, model context, outputs and tool calls.
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Set the security boundary before you write a prompt
The first prompt should not ask for a database schema or a user interface. It should establish what the application is allowed to know and do. Record these decisions in the design and backlog so that an AI-generated feature can be checked against them.
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Inventory data and classify its sensitivity
List every data set the app will store, retrieve, calculate or display. Mark whether each class is public, internal, confidential, regulated or restricted to a named team. Include temporary data such as uploaded files, prompt content, model context, logs, exports and backups.
For each class, answer:
- Where is the authoritative source?
- Which roles may read, create, update or delete it?
- May the data be sent to an external model or connector?
- How long may it remain in the app, logs, caches and backups?
- What must happen if a user requests more data than their role allows?
Map identities, systems and actions
Draw the application’s trust boundaries. Identify users, service identities, model providers, retrieval stores, APIs, file shares, queues and administrative interfaces. Then enumerate actions, not just screens: read a customer record, approve an expense, export a report, send an email, create a ticket, change a policy or delete a file.
Classify actions as read-only, reversible write, irreversible write, external disclosure or system administration. This classification determines whether an automated step is acceptable or must stop for human approval.
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Turn broad goals into testable requirements. Examples include:
- “A manager can view expenses for their own cost centre but cannot query another cost centre.”
- “The model may retrieve policy documents from the approved library but not employee medical records.”
- “An external email containing confidential data requires named approval and is recorded in an audit log.”
- “No credential, API key or connection string is stored in source code, prompts or client-side configuration.”
- “Every failed authorization attempt is logged with the user, resource, action and decision.”
These requirements are the acceptance criteria for both human developers and AI-generated work. A prompt that asks for broader access than the requirements permit is a design change, not a harmless implementation detail.
Give the model only the context it needs
Keep secrets and unnecessary business data out of prompts
Never paste passwords, private keys, access tokens, connection strings or production credentials into a prompt. Do not include a full customer export when a five-row synthetic example will demonstrate the desired format. Replace names, account numbers and other identifiers with realistic test values.
Use a secure secrets store and inject credentials at runtime through the platform’s supported mechanism. Keep secrets out of source control, notebooks, generated documentation, screenshots and error messages. Rotate a credential immediately if it has appeared in a prompt, log or generated file.
Check the model provider’s data terms
Before using company information, verify how the provider handles prompts, uploaded files, outputs, retention, access by personnel, regional processing, model training and deletion. The provider’s terms must meet your organization’s data-protection, contractual and regulatory requirements. An enterprise label alone is not evidence that every data class is permitted.
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Use approved retrieval sources
At runtime, allow retrieval only from explicitly approved repositories. Apply the repository’s permissions when searching and displaying results; do not create a single shared identity that can read everything and then rely on the model to hide sensitive passages.
Defend against prompt injection in retrieved content. Treat documents, emails, tickets and web pages as untrusted data, not as instructions. Separate system rules from retrieved text, limit tool calls, validate arguments and reject requests that violate the app’s authorization policy.
Enforce least privilege at every layer
Least privilege is a design constraint for users, connectors, models, service identities and individual app components. Grant only the operations required for a task, for the shortest practical period, and review the assignment as the workflow changes.
Authenticate users and authorize each request
Use the organization’s approved identity provider and require appropriate multifactor authentication. Check authorization on every server-side request, not only when a screen loads. Do not trust role, department or record identifiers supplied by the browser or by a model.
Enforce permission at the data source as well as in the user interface. If a user can bypass the app and query the underlying database, file store or API, a hidden button is not a security control.
Prefer narrow service identities
Create separate identities for separate workflows and environments. A report generator should not use the same identity as an administrator. Remove unused permissions, restrict which resources an identity can reach and avoid broad shared accounts that make attribution impossible.
Use row-, record- and field-level controls
For sensitive business data, authorization may need to distinguish records and columns. Microsoft’s Power Platform guidance, for example, describes Dataverse role-based and column-level security and recommends sharing apps and data only with people who need access. Apply the equivalent controls in your chosen platform, and test them with users in every relevant role.
Microsoft Entra guidance reports that more than 90% of identities use less than 5% of the permissions granted, and that more than 50% of those permissions are high risk. These are Microsoft-attributed telemetry figures; the cited page does not state the collection year or methodology in the visible passage, so treat them as a warning about permission sprawl rather than a universal benchmark.
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Constrain connectors, network access and write operations
Make an integration inventory
List each connector, API, plug-in, package, model, retrieval index and external service. For each item, record its owner, identity, scopes, network path, data exchanged, failure behavior, logging and renewal date. Remove experimental integrations before release.
| Control area | Safer default | Questions to test |
|---|---|---|
| Identity | Dedicated, non-admin identity with narrowly scoped permissions | Can it read or change anything outside this workflow? |
| Network | Allowlisted endpoints and private connectivity where supported | Can the app call arbitrary domains or upload unrestricted files? |
| Data movement | Approved sources, minimal fields and explicit retention | Does a prompt, log or export contain more data than the task needs? |
| Writes | Read-only until the workflow is proven; bounded updates afterward | Are destinations, record counts and values validated? |
| Dependencies | Version-pinned packages and reviewed model components | Are vulnerabilities, license changes and updates monitored? |
| Audit | Central records of access, tool calls, approvals and configuration changes | Can an investigator reconstruct who did what and why? |
Limit outbound and high-impact actions
Start an AI workflow in read-only mode. Add writes one operation at a time, with allowlists for destinations, fields, file types, amounts and record counts. Validate model-produced arguments against a strict schema; never concatenate model text into a command, query or URL without appropriate parsing and validation.
Require a human pause before an action that transfers data outside the organization, changes system configuration, approves money, deletes information, contacts a customer or changes a user’s access. Microsoft’s guidance specifically recommends human review before high-risk operations such as external data transfers and system configuration changes.
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Show the reviewer the exact action, affected records, destination, data fields, reason, model output and policy checks. Do not ask a person to approve an opaque “run automation” button. Record the approver, timestamp, decision and any edits, and expire an approval if the underlying data or action changes.
Review AI-generated code and configuration
AI output is a draft. A reviewer should be able to explain every permission, data flow, dependency and failure path before release.
Use a focused review checklist
- Input handling: Are prompts, form fields, files, API parameters and retrieved text validated, size-limited and safely encoded?
- Output handling: Is model output treated as untrusted data before it is rendered, stored, executed or passed to another tool?
- Authorization: Is access checked server-side for every object and action, including background jobs and exports?
- Secrets: Are credentials absent from source, logs, prompts, test fixtures and client bundles?
- Queries and commands: Are parameters bound and commands restricted to an allowlist rather than assembled from model text?
- Errors: Do failures avoid leaking records, tokens, stack traces or internal topology to users?
- Dependencies: Are package versions pinned, scanned and sourced from approved registries?
- Logging: Are security events recorded without copying sensitive payloads into logs?
- Resilience: Does the app fail closed when the model, connector, policy service or identity provider is unavailable?
Ask the model for review artifacts, not just code
Useful prompts request a threat model, data-flow diagram, permission matrix, abuse cases, test cases and explanation of assumptions alongside an implementation. Ask the model to identify uncertainty and list controls it could not verify. A second review pass can look for authorization bypasses, insecure defaults and data leakage, but it does not replace an independent human review.
Test adversarially
Test with unauthorized users, manipulated record identifiers, oversized inputs, malicious files, injected instructions in retrieved documents, expired sessions, connector failures and model refusals. Verify that the app neither performs the action nor reveals sensitive details when a policy check fails.
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Traditional application threats remain relevant, but the model and its context create additional trust boundaries. Document assets, actors, entry points, threats, mitigations and residual risk for each workflow.
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| Threat | Example | Practical mitigation |
|---|---|---|
| Prompt injection | A support ticket tells the assistant to ignore its rules and export a customer list | Separate instructions from data, constrain tools, validate requests and require approval for disclosure |
| Excessive agency | An agent can delete records because a connector inherited administrator rights | Use a dedicated identity, narrow scopes, bounded operations and reversible workflows |
| Data leakage | A prompt or log stores confidential case details longer than permitted | Minimize context, redact fields, set retention and review provider terms |
| Broken authorization | A user changes an ID in a URL and sees another team’s record | Enforce object-level checks at the data source and test every role |
| Unsafe output execution | Generated text is inserted into a query or shell command | Use typed schemas, parameterized interfaces and allowlists; never execute free-form output |
| Supply-chain compromise | Generated code adds an unreviewed package or model component | Pin versions, scan dependencies, review provenance and approve updates |
| Audit failure | A file transfer occurs but no one can identify the initiating user or approver | Log identity, action, target, policy decision, approval and outcome centrally |
Build security tests into the release process
Test the security contract
Convert each requirement into an automated or repeatable test. A permission test should verify both allowed and denied cases. A data-loss test should confirm that restricted fields are excluded from prompts, responses, exports and logs. An approval test should prove that a high-impact action cannot proceed when approval is absent, expired or revoked.
Scan code, dependencies and configuration
Use the organization’s approved static analysis, dependency scanning, secret detection and infrastructure checks. Review findings rather than blindly suppressing them. Scan generated configuration as well as generated source code: an insecure sharing setting can be as damaging as a vulnerable function.
Separate environments
Keep development, test and production identities, data and connectors separate. Use synthetic or masked data in development. Require a controlled promotion path, peer review and an auditable change record before production deployment.
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Do not release until the owner can show:
- an approved data-flow and threat model;
- a current permission and connector inventory;
- successful authorization, input-validation and abuse-case tests;
- reviewed generated code, configuration and dependencies;
- secret and sensitive-data checks with no unresolved critical findings;
- logging, alerting, backup and recovery procedures;
- a named owner for incidents and permission reviews.
Monitor and maintain the app after launch
Security is a lifecycle activity. New users, changed connectors, model updates, business rules and imported documents can alter the risk even when application code does not change.
Monitor useful signals
- Authentication failures, unusual locations and impossible travel where applicable
- Denied authorization attempts and sudden privilege changes
- Unusual prompt volume, retrieval breadth, export size or tool-call frequency
- New connectors, changed scopes, modified sharing settings and configuration changes
- External transfers, bulk updates, deletion requests and approval overrides
- Model refusals, policy violations, validation failures and repeated retries
Log enough context to investigate—identity, resource, action, policy decision, approval and result—without placing complete confidential prompts or documents in a broadly accessible log store. Protect logs with restricted access, retention rules and tamper detection.
Review permissions and threats on a schedule
Re-certify access when people change roles and at a defined interval for sensitive applications. Remove dormant identities and unused connectors. Re-run the threat model whenever the app gains a new data source, model, tool or write capability. Feed incidents and test findings back into requirements and backlog items.
Prepare recovery
Document how to disable an agent, revoke a connector, rotate credentials, quarantine a data source, restore records and notify affected parties. Test the emergency stop and recovery process before an incident. A workflow that cannot be quickly disabled is too powerful for unattended operation.
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Choosing an AI-building approach by security control
There is no universally safest category. Compare approaches by the controls your organization can configure and operate, not by how quickly a prototype appears.
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| Decision axis | Questions | What a stronger option provides |
|---|---|---|
| Identity and data permissions | Can access follow the user and be enforced at the source? | Granular roles, record or field controls, separate service identities and regular reviews |
| Isolation and network boundaries | Can development, test and production be separated? | Private connectivity, endpoint allowlists and environment-level controls |
| Connectors and writes | Can tools be limited to approved operations? | Read-only defaults, scoped actions, schemas, quotas and approval gates |
| Code and configuration review | Can generated artifacts be exported, versioned and tested? | Source control, peer review, automated scans and reproducible deployments |
| Audit and monitoring | Can investigators reconstruct access and actions? | Central, protected logs for identity, data access, tool calls and approvals |
| Compliance fit | Can retention, residency, deletion and incident processes be demonstrated? | Documented provider terms, configurable policies and evidence for audits |
A low-code platform may supply useful identity, role, audit and lifecycle features, but configuration and organizational process still determine whether those controls are effective. “Low-code” describes how an app is assembled, not its risk level.
A practical end-to-end workflow
- Define the boundary. Document data classes, users, systems, permitted actions, prohibited actions and acceptable residual risk.
- Prepare safe context. Use synthetic examples, remove secrets and confirm provider data-use terms before sharing company information.
- Design permissions first. Create roles, source-level restrictions, service identities and environment boundaries before generating features.
- Prototype read-only. Ask the AI for screens, schemas, tests and explanations while connectors expose the minimum approved data.
- Review generated artifacts. Inspect code, configuration, dependencies, prompts, retrieval rules, error handling and logging against the security requirements.
- Add writes gradually. Validate typed arguments, constrain destinations and values, and make high-impact operations require explicit human approval.
- Threat-model and test. Exercise prompt injection, authorization bypass, data leakage, malformed input, connector failure and abuse cases.
- Release through a gate. Promote reviewed artifacts from isolated test environments only after critical findings are resolved and monitoring is ready.
- Operate and improve. Monitor activity, review permissions, rotate secrets, reassess new integrations and update controls after incidents or model changes.
Common mistakes and how to correct them
“It is internal, so authentication is enough.”
Why it fails: A valid employee account can still be over-privileged, compromised or assigned to the wrong records.
Correction: Enforce least privilege and object-level authorization at every request, then test denied cases.
“The model wrote it, so the platform must have checked it.”
Why it fails: Generation is not a security review, and platform defaults cannot understand every business rule.
Correction: Require human code and configuration review, automated scans and threat-focused tests.
“We pasted real data to get a better answer.”
Why it fails: The prompt may violate policy or expose more information than the task requires.
Correction: Minimize and redact context, use synthetic examples and verify provider terms before processing sensitive data.
“The agent only sends email.”
Why it fails: Email can disclose regulated or confidential information to an external recipient.
Correction: classify outbound communication as a consequential action, validate recipients and content, and require approval when risk is high.
“The user interface hides the admin button.”
Why it fails: A caller may invoke the underlying API directly or alter a request parameter.
Correction: enforce authorization server-side and at the data source, independent of the interface.
“We can add logging later.”
Why it fails: Without early audit data, you cannot prove what happened or investigate a leak.
Correction: design protected, privacy-aware audit events before the first production release.
What “secure enough to launch” means
An AI-assisted internal app is ready for a controlled launch when its allowed data and actions are explicit, permissions are narrow and source-enforced, prompts and runtime context are minimized, connectors and writes are bounded, high-impact actions have meaningful human approval, and generated code and configuration have passed review and testing.
That decision is not permanent. Treat every model, connector, data source and new automation as a change to the threat model. The safest pattern is to let AI shorten drafting and iteration while people retain responsibility for requirements, authorization, release approval and ongoing operations.
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