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The safest approach is to decide what each data category may be used with, and which approved AI feature may handle it, before anyone pastes, uploads or connects project information. AI services can receive far more than typed prompts: depending on the product and account configuration, they may process uploaded files, conversation history, connected repositories, or live project and model context. Treat every enabled feature as a data-processing path that needs an owner, limits and monitoring.
This guide gives construction owners, project managers, IT and security staff, and design teams a practical control system: classify information, verify the exact feature and contract, minimize inputs, restrict access and integrations, train users, and prepare for an accidental disclosure or malicious event. No AI platform is automatically safe; protections and data terms vary by product, feature, edition, configuration and contract.
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Why AI use changes the project-data risk
A conventional document may stay in a controlled file system. An AI request can create additional copies or processing locations. The service may receive the text you type, an uploaded drawing or spreadsheet, previous turns in the conversation, files reached through a connector, or context from the application in which the assistant runs.
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Confidentiality is only one part of the risk. A manipulated prompt, over-permissioned agent or compromised account can affect the integrity of a schedule, estimate or design decision. An outage, disabled integration or deleted history can affect availability. NIST’s proposed Control Overlays for Securing AI Systems project (COSAiS) organizes AI security around confidentiality, integrity and availability across generative AI, predictive AI, AI agents and developer security. The overlays are a project in development, not a certification or completed standard.
NIST’s National Cybersecurity Center of Excellence states the core issue plainly: “Data confidentiality concerns data in storage, during processing, and while in transit.” Your controls must address all three states rather than focusing only on what a user sees on screen.
Classify information before anyone uses an AI tool
Start with an inventory owned by the project, information-security and legal teams. Record where each category is stored, who owns it, contractual restrictions, and the highest handling level it receives. The labels below are a policy starting point; your client agreements and governing regulations may require stricter treatment.
| Project information | Examples | Default AI handling | Approval needed |
|---|---|---|---|
| Public or intentionally released | Published project description, approved marketing copy, public planning information | May be used only in an allowlisted account and feature; confirm that no hidden project context is attached. | Project owner or communications owner |
| Internal operational | Generic meeting agendas, internal procedures, non-sensitive status summaries | Use a managed account after the feature’s retention, training and sharing settings are verified. | Department manager |
| Confidential project | BIM/CAD models, unpublished plans and specifications, schedules, bids, cost data, change-order analysis | Do not upload by default. Permit only a reviewed feature with contractual and technical safeguards that match the project. | Project owner plus security or IT review |
| Personal or sensitive | Worker, occupant, visitor or subcontractor names, contact details, photographs, video, identifiers and access records | Use the minimum necessary fields. Prefer redaction or synthetic examples; obtain privacy and client approval where required. | Privacy or legal owner plus project owner |
| Security-critical | Credentials, tokens, keys, alarm details, access-control layouts, vulnerable-site information and emergency procedures | Prohibit in general-purpose AI features. Handle through approved security systems and narrowly scoped automation only. | Security authority |
| Client- or government-furnished | Information supplied under a client, public-sector or defense contract, including marked material | Follow the contract, markings, flow-down terms and authorized environments. Do not infer permission from an ordinary business account. | Contracting authority and qualified compliance or security counsel |
Check obligations alongside the classification
For every category, identify confidentiality clauses, intellectual-property ownership, privacy duties, retention requirements, security specifications, export or location restrictions, and restrictions on subcontractors or service providers. Capture the answer in the project data register, not only in an informal email.
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Some construction work may involve Controlled Unclassified Information (CUI), but ordinary construction drawings are not automatically CUI. NIST describes Special Publication 800-171 as requirements for protecting CUI in nonfederal systems. Whether it applies depends on the contract, the information’s designation and the surrounding context. If CUI may be present, pause the proposed AI workflow until the contracting authority or qualified compliance and security counsel establishes the applicable requirements and authorized service boundary.
Approve the exact tool, account and feature
Maintain an allowlist that names the provider, product, edition or account tier, enabled features, permitted data classes, project owner and review date. “We use an approved AI vendor” is not specific enough: a chat feature, file-analysis feature, plug-in and autonomous agent may have different data paths and permissions.
Questions for the provider and your contract review
- What enters the service: typed prompts, attachments, conversation history, connected files, application telemetry, or model and design context?
- Where are prompts, files, outputs and logs stored, and for how long? Can the customer set retention or verify deletion?
- Are customer inputs or outputs used for model training, quality review or product improvement? Is the choice account-wide or feature-specific?
- Can provider employees, support staff or subcontractors access content, under what authorization and logging?
- What account-level sharing, workspace discovery and export functions are enabled?
- What encryption, authentication, session-management and administrator controls are available?
- Which integrations, connectors, plug-ins or agents can read files or take actions, and can permissions be restricted to a project or folder?
- Where is data processed and transferred, including backup and support locations?
- What contractual commitments cover confidentiality, subprocessors, incident notification, deletion, legal requests and audit evidence?
- How are material changes to the feature, terms, model, retention or data location communicated?
Record the answers, the evidence reviewed and the person who accepted the residual risk. Re-check the record when a feature, account configuration, contract or project scope changes. A transparency card or similar feature-specific disclosure is more useful than a general statement that a provider “takes AI security seriously.”
Compare services on a consistent scorecard
| Comparison axis | What to record | Decision question |
|---|---|---|
| Data received | Prompts, uploads, connected repositories, conversation history and model context | Does the feature receive only the material needed for this task? |
| Retention and model use | Storage duration, deletion process, backups, human review and training or improvement use | Can the project meet its contractual retention and confidentiality duties? |
| Identity and sharing | Managed identities, role-based access, multifactor authentication, workspace sharing and audit logs | Can access be limited to the right people and investigated later? |
| Integrations and agents | Files, APIs, actions, scopes, approval gates and token revocation | Can a connector or agent do more than the use case requires? |
| Security and incident process | Security documentation, vulnerability reporting, monitoring and notification commitments | Will you know what happened and be able to contain it? |
| Contract and location | Customer terms, subprocessors, processing regions and deletion obligations | Are the terms compatible with the client, project and regulatory requirements? |
This scorecard supports a risk-based decision; it does not establish a universal ranking. A feature that is suitable for a public progress summary may be unsuitable for an unpublished bid, even within the same provider.
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Minimize and sanitize every input
Send the smallest excerpt, field set or image area that can answer the question. The person preparing the prompt should be able to explain why each included item is necessary.
- State the task without attaching the whole project. Ask for a formula, outline or classification rule before sharing a complete model, estimate or document set.
- Extract only required fields. For a schedule question, provide activity IDs, dates and dependencies rather than employee names, access notes and unrelated cost data.
- Remove high-impact material. Exclude passwords, API keys, tokens, private keys, unpublished pricing, personal identifiers, security layouts, alarm information and client-confidential text unless a specifically approved workflow requires them.
- Redact or synthesize examples. Replace a real address with a project code and use invented quantities to test a prompt or template.
- Inspect images and metadata. Crop site photos to the area needed, remove embedded GPS and camera metadata where appropriate, and check that whiteboards, badges, screens and documents in the background are not visible.
- Review the output before reuse. Do not paste an AI response into a submittal, change order, safety communication or design record without a qualified person checking facts, calculations, permissions and sources.
Removing names is not a guarantee of anonymity. NIST identifies AI-related re-identification risks: combinations of dates, locations, roles, rare events, images and project details may identify a person or site. De-identification also does not override a contract that prohibits sending the underlying client information to a third party.
Control accounts, devices and integrations
Use managed identities and least privilege
- Require company-managed accounts, devices and browsers for project AI work; prohibit personal accounts for project material.
- Use role-based access and multifactor authentication. Give users access to only the projects, folders and features required for their role.
- Separate personal experimentation from production project workspaces and disable public sharing and anonymous links.
- Log prompts, uploads, downloads, administrative changes and connector activity to the extent the feature and law permit. Protect logs because they may contain sensitive content.
- Review membership when a worker, consultant or subcontractor changes role, and promptly revoke accounts, sessions, API keys and tokens when access ends.
Inspect connectors and agents before enabling them
A connector that can search a document repository may expose every file visible to its service account. An agent that can write to a schedule, issue a message or modify a record introduces integrity and availability risk in addition to confidentiality risk. For each integration, document the exact repositories, actions, scopes, approval gates, service identity and revocation method. Start with read-only access and a small project folder where possible; expand only after a documented review.
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Assign clear owners rather than leaving decisions to individual users.
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- Project owner: identifies business purpose, approves the data classes and accepts residual project risk.
- IT or security: validates identity, access, logging, integration scopes, technical safeguards and incident procedures.
- Legal, privacy or contracts: checks confidentiality, intellectual-property, privacy, location, subcontractor and flow-down obligations.
- Design and delivery leads: define what review is required before an AI-assisted output enters a drawing, specification, estimate, schedule, safety record or client communication.
- Users and subcontractors: follow the allowlist, minimize inputs, preserve records required by the project and report mistakes immediately.
Put the decision in a short, searchable register containing the use case, approved feature and account, permitted data class, prohibited content, owner, review date, provider evidence, contract reference and response contact. Require a new review when a project adds a data category, a provider changes retention or training terms, an integration gains write access, or the AI feature begins using model context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Train people with concrete examples
Training should show the exact boundary, not just say “be careful.” A useful exercise contrasts these requests:
- Usually lower risk after approval: “Rewrite this public project description for a community notice.”
- Requires controlled handling: “Identify schedule conflicts from these activity IDs and dates,” with names, cost fields and security notes removed.
- Prohibited in a general-purpose feature: “Summarize this file containing access credentials, alarm locations and emergency vulnerabilities.”
Explain that a prompt can be exposed through chat history, uploaded files, connected sources, logs or an account shared with others. Require users to confirm the destination account before sending, check attachments and recipients, and preserve the approved source record when an AI output informs a project decision.
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Prepare for an accidental upload or malicious event
Use an incident playbook that covers both a mistaken disclosure and an attack on the AI system or its connected data.
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- Contain: stop the session, disable the connector or agent, revoke tokens and sessions, remove shared links and quarantine affected copies where the service permits.
- Preserve evidence: record the account, feature, time, prompt or file, recipients, permissions, output and relevant logs. Do not delete the only evidence while trying to clean up.
- Classify the exposure: identify the data category, project and contract, whether personal data or CUI may be involved, and whether the content was retained, indexed, downloaded or used for model improvement.
- Notify the right owners: alert project leadership, security, legal or privacy contacts, the client or contracting authority when required, and the provider through its incident channel.
- Assess integrity: check whether prompts, outputs, schedules, designs, messages or connected records were altered or generated from untrusted instructions.
- Recover and improve: restore trusted records, rotate exposed credentials, narrow permissions, document notification decisions and update the allowlist or training.
CISA’s joint guidance on deploying AI systems securely, dated April 15, 2024, frames operational protection, detection and response for malicious activity against AI systems and related data and services. Verify current agency guidance when maintaining your playbook. NIST’s confidentiality practice guide likewise emphasizes identifying assets, protective technologies, detection, response and recovery planning.
A repeatable approval workflow
Use this sequence for a new AI use case or a material change to an existing one:
- Describe the task and outcome. Name the project decision or work product, the responsible person and whether the output is advisory or can trigger an action.
- List every input path. Include typed text, attachments, history, connected files, application context, telemetry and agent permissions.
- Classify the data and obligations. Check client terms, privacy duties, intellectual-property restrictions, security clauses and any CUI designation.
- Select an allowlisted feature. Complete the provider and contract review, configure retention, sharing and training choices, and document the evidence.
- Reduce the input. Redact, crop, aggregate or synthesize until the remaining material is necessary and permitted.
- Test safely. Use a non-production sample, verify access and logs, and confirm that the output cannot take an unintended action.
- Operate and review. Assign a human reviewer, monitor use, revoke access when roles change and revisit the decision when the feature, terms or project changes.
What good protection looks like on a live project
An owner might approve an AI feature to draft a public progress update using released milestones. The same owner could require a separate review before a design team uses a model-context assistant to analyze an unpublished BIM model. A cost engineer might be allowed to process a redacted table of quantities but not bid tabs containing subcontractor pricing. A site manager could use a synthetic incident example for training while keeping real worker identities and security footage out of the service. These are policy decisions tied to data class and feature behavior, not blanket judgments that all AI is safe or unsafe.
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