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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAI can improve a family office’s research and operations, but it can also expose confidential data, magnify errors and create legal, security and investment risks. The practical answer is disciplined governance: assign an accountable owner, approve and tier use cases, protect data, require expert review of consequential outputs, and diligence AI companies on their data rights, dependencies, controls and durability—not on a product demonstration alone.
Why AI is both an opportunity and a risk
AI can accelerate document review, portfolio research, reporting, coding, tax-workpaper preparation and other repetitive tasks. It may also produce confident errors, leak sensitive information to an external service, embed bias or become unavailable when a vendor changes its model or pricing. Those are not separate technology and investment issues: an office that uses AI internally may become dependent on the same vendors, data sources or model providers that shape the value of an AI company in its portfolio.
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The OECD’s 2024 analysis surveyed regulatory approaches to AI in finance across 49 OECD and non-OECD jurisdictions. Its conclusion captures the two-sided risk: “At the same time, AI in finance could also amplify existing risks in financial markets and create new ones.” The report describes a multi-jurisdiction landscape, not a single set of family-office rules.
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How should a family office govern AI?
Governance should be proportionate to the consequence of an output and the sensitivity of the data involved. PwC family-office guidance emphasizes values-aligned policy, risk-tiered use cases, qualified human review, privacy-conscious handling, access governance, monitoring of external tools and staff training. Morgan Lewis similarly points to documented use cases and risk assessments, approved tools, clear ownership, secure infrastructure and vendor management. These are professional recommendations, not automatically applicable laws.
1. Name an accountable executive owner
Put one senior person in charge of the AI register, approvals, incidents and reporting to the governing body. The owner can coordinate technology, investment, legal, privacy and operations specialists, but responsibility should not be assigned to “the team” or left with an informal power user. Record who approves a use case, who owns the vendor relationship and who can suspend access.
2. Create a use-case inventory before deployment
Record each proposed use in a register. At minimum, capture the purpose, users, data types, model or vendor, output recipient, decision affected, review requirement, retention setting and fallback process. A short internal summary of a public document is materially different from an output that influences a trade, tax filing, legal position or communication to a family member.
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| Risk tier | Typical example | Minimum control | Escalation trigger |
|---|---|---|---|
| Low | Formatting or summarizing non-confidential material | Approved tool, basic user training and a check for obvious errors | Confidential data, external distribution or a change in purpose |
| Medium | Drafting research notes, management reports or internal analysis | Documented source material, reviewer sign-off and version retention | Output becomes an input to an investment, tax, legal or family decision |
| High | Trade recommendations, beneficiary decisions, tax or legal conclusions, or sensitive family communications | Qualified professional review before action, traceable evidence and restricted access | Material error, unexplained output, data incident or inability to reproduce the result |
3. Approve tools and control access
Maintain an allowlist of services that have been assessed for confidentiality, retention, reuse, security and administrative controls. Use role-based access, single sign-on where available, multifactor authentication and prompt removal of access when staff or contractors leave. Separate experimentation from production workflows. A browser plug-in or consumer account should not silently become an approved system for investment or family data.
4. Define data-handling rules
Classify information before it enters a model. The map should include personal and family information, beneficiary records, tax material, investment positions, private-company information, credentials and confidential correspondence. For every tool, document where data is processed, who can access it, how long it is retained, whether it is reused for training, how it is deleted and which subcontractors receive it. Restrict copying from source systems, mask identifiers where practical and prohibit secrets or credentials in prompts.
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5. Require qualified human review for consequential outputs
Review is more than asking whether an answer “looks right.” The reviewer should check the underlying sources, calculations, assumptions, date sensitivity, omissions and conflicts of interest, then record approval or correction. For an investment memo, retain the documents and data used to reach the conclusion. For tax or legal work, the responsible professional—not the model—must make the determination.
6. Monitor vendors, models and changes
Track model version, material changes to terms, outages, security notices, pricing, usage limits and changes in data-retention practices. Test important workflows after a model update. Keep a manual or alternate-provider procedure for critical activities so a withdrawn model or service interruption does not halt reporting, trading operations or required communications.
7. Train people and rehearse incidents
Training should cover hallucinated citations, prompt injection, sensitive-data exposure, deepfakes, phishing and the office’s escalation path. Run a tabletop exercise for a leaked prompt, a materially wrong recommendation and a vendor outage. Preserve logs consistent with the office’s privacy and retention obligations, and notify the owner, security lead and counsel according to the incident plan.
“What governance mechanisms are in place to oversee AI systems?”
That question comes from Caroline Crenshaw, an SEC commissioner, in personal remarks at the SEC’s March 27, 2025 roundtable on artificial intelligence in the financial industry. She asked: “What governance mechanisms are in place to oversee AI systems – especially those systems that may employ ‘black box’ algorithms, where it’s not clear how inputs are weighed or outputs derived?” Her remarks state that the views are her own and are not necessarily those of the Commission, fellow commissioners or SEC staff.
For a family office, the question translates into an evidence request. Can the office identify the data and model used, explain the approval path, reproduce a result, detect a change in model behavior and show who accepted the risk? If the answer is no, limit the use to low-consequence tasks until controls improve. Black-box performance claims are not a substitute for an audit trail.
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“What disclosures are being made around AI uses and risk?”
Crenshaw also asked: “What disclosures are being made around AI uses and risk, and are they consistent and sufficient?” A family office should decide what it tells family members, trustees, investment committees, counterparties and service providers about material AI use. The appropriate disclosure depends on the office’s role, contracts and jurisdiction, but consistency matters: an investment report should not imply that a professional performed analysis that was generated without the required review.
Keep a plain-language description of material uses, the human review performed, known limitations and the person responsible for questions. Do not claim that an output is independent, verified or generated from proprietary information unless the office can substantiate that statement.
Can a family office use AI for investment decisions?
It can use AI as an assistive tool, subject to its legal status, mandate, controls and the quality of the underlying information. A model should not become an unexamined decision-maker. Use a staged workflow:
- Define the decision. State whether the task is screening, document extraction, scenario analysis, monitoring or a recommendation. Set the materiality threshold and prohibited uses.
- Control the inputs. Use approved, dated sources and document licensing or access rights. Remove unnecessary personal and confidential information.
- Run the analysis. Record the model, version, prompt or configuration, data set and time of execution. Separate generated text from verified facts.
- Challenge the result. Have a qualified reviewer test calculations, citations, missing information, alternative explanations and conflicts. For high-impact decisions, obtain independent analysis rather than merely editing the model’s output.
- Decide and document. The authorized human makes the decision and records why the AI output was accepted, corrected or rejected.
- Monitor after the decision. Check for drift, stale data, changed assumptions and outcomes that reveal a systematic error.
This process preserves judgment and stewardship. As Richa Bahl, PwC’s Family Enterprise Advisory Leader for Canada, put it: “AI doesn’t eliminate the need for talent in family offices, it raises the bar, shifting value from execution to judgment, stewardship, and trust.”
What should a family office check before investing in an AI company?
Diligence should test whether the company can sustain value if a foundation-model provider changes access, pricing or capabilities. Product demonstrations show what works in a selected scenario; they do not establish lawful data rights, security, margins, accuracy or defensibility.
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| Diligence area | Questions to ask | Evidence to request | Warning signs |
|---|---|---|---|
| Data provenance and training rights | Where did training and operating data come from? Does the company have permission to use it, and can rights survive a customer or vendor dispute? | Data-source register, licenses, consent records and contractual restrictions | Unclear ownership, broad claims unsupported by contracts or dependence on scraped material of uncertain status |
| Intellectual property | Who owns code, model modifications, prompts, outputs and customer-specific work? Are employee and contractor assignments complete? | Assignment agreements, open-source inventory and third-party license review | Unresolved ownership, incompatible licenses or material claims from contributors |
| Model dependency | Which external models, chips or cloud services are essential? What happens if access, price or performance changes? | Architecture diagram, supplier contracts, substitution plan and cost sensitivity | One-provider concentration with no tested fallback or economics that fail after a price increase |
| Security and confidentiality | How are customer prompts, outputs and credentials protected? Can one tenant access another’s data? | Security assessments, access logs, incident history and segregation design | Overbroad privileges, weak tenant isolation or no tested incident process |
| Accuracy and explainability | How is performance measured in the intended workflow? What are error rates, known failure cases and human-review requirements? | Evaluation protocol, representative test results, change logs and escalation rules | Only benchmark or demo results, no domain testing or claims that errors are impossible |
| Governance and regulation | Who owns risk, approves releases, handles incidents and assesses sector-specific obligations? | Policies, committee records, compliance assessments and customer terms | No accountable owner, undocumented releases or a promise that regulation does not apply |
| Defensibility and workflow fit | What remains valuable beyond access to a general model? | Retention data, integration maps, domain expertise, proprietary processes and customer evidence | Easy-to-copy features, weak switching costs or no proof of sustained workflow adoption |
These are diligence prompts, not a conclusion that every AI company possesses each asset. Ask management to distinguish proprietary data from data merely accessible through a supplier, and recurring revenue from pilot commitments. Stress-test gross margins, support costs, compute usage and the effect of model-provider terms before assigning a valuation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Legal and regulatory coverage is specific to the office
AI does not determine legal status by itself. The relevant questions are which entity is acting, what activity it performs, whom it serves, whether it delegates a function and which jurisdiction governs. In the U.S. context described by the OECD, obligations applying to covered brokers, dealers, investment advisers, registered investment companies and other regulated participants may apply whether or not AI is used.
The OECD describes investment advisers’ duties of care and loyalty, including acting in clients’ best interests and meeting relevant disclosure duties. That description does not establish that a particular family office is an investment adviser, nor does it resolve the treatment of a trust, single-family office, multi-family office, fund vehicle or outsourced manager. Obtain advice on the office’s legal entity, activities and jurisdiction before stating that a rule applies or does not apply.
Use the SEC roundtable remarks as issue-spotting questions rather than binding rules: how the system works, who oversees it, how duties and trading or back-office requirements are met, what is disclosed, whether users or investors could be exposed to fraud or systematic disadvantage, and whether AI could create market or volatility risks.
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OECD’s 2024 work treats privacy and data governance as related AI concerns whose treatment differs across jurisdictions and legal systems. A family office operating across borders should therefore avoid assuming that one consent, transfer mechanism or retention policy works everywhere.
- Map each data category, origin, purpose, destination and access group.
- Identify whether the vendor retains prompts and outputs, reuses them for training or permits subcontractor access.
- Set deletion, export and legal-hold procedures before production use.
- Separate family, beneficiary, tax, investment and confidential business information where practical.
- Review cross-border transfers, contractual safeguards and local professional-secrecy requirements with counsel.
These controls are prudent governance steps. They should not be presented as a universal legal checklist without confirming the applicable jurisdiction and role.
How to compare AI vendors or AI-company investments
Use a common scorecard so a compelling demonstration does not outweigh a serious control gap. Score each candidate against the same evidence standard and record assumptions.
| Comparison axis | What a strong file should show |
|---|---|
| Data provenance and lawful access | Traceable sources, documented rights and restrictions that match the intended use |
| Security and confidentiality | Least-privilege access, tenant separation, encryption, monitoring and tested incident response |
| Provider independence | Architecture and contracts that allow a practical change of model or infrastructure provider |
| Accuracy and review | Domain-relevant testing, known limitations, explainability appropriate to the decision and defined human review |
| Governance and readiness | Named owner, release controls, incident handling, records and a plan for applicable regulation |
| Workflow and defensibility | Deep integration, proprietary information, domain expertise or operational know-how that is difficult to copy |
A practical implementation roadmap
First 30 days: establish control
- Appoint the executive owner and a cross-functional review group.
- Inventory current tools, accounts, data flows and informal uses.
- Freeze unapproved high-impact uses while they are assessed.
- Publish a short acceptable-use policy and an incident contact.
Days 31–60: assess and pilot
- Tier use cases by consequence and data sensitivity.
- Review vendor terms, retention, security and access settings.
- Pilot low- or medium-risk workflows with documented human review.
- Test a failure case, an outage and a model update before expanding use.
Days 61–90: operationalize
- Approve or reject pilots using recorded evidence and residual-risk owners.
- Set recurring access, vendor and model-change reviews.
- Train staff and contractors by role, including investment and family-facing teams.
- Report incidents, exceptions, review completion and material changes to the governing body.
Common failure modes and fixes
- Shadow AI: Staff use personal accounts for confidential work. Fix: provide approved tools, block prohibited channels where appropriate and make reporting a safe alternative to concealment.
- Demo-driven investment: A polished prototype substitutes for diligence. Fix: require data-rights, dependency, security, accuracy and margin evidence.
- Rubber-stamp review: A reviewer checks grammar but not sources or calculations. Fix: use a written review checklist tied to the decision’s consequences.
- Unclear accountability: Technology, investment and legal teams each assume another owns the risk. Fix: name one executive owner and assign control owners in writing.
- Stale controls: A model or vendor changes after approval. Fix: monitor changes, retest material workflows and retain a fallback process.
- Overbroad legal conclusions: The office assumes a rule applies—or does not apply—because AI is involved. Fix: analyze entity, activity, delegation and jurisdiction with counsel.
What the governing body should receive
A concise quarterly report can include the approved-use register, high-risk uses, exceptions, access reviews, vendor and model changes, incidents, review completion, training status and open remediation items. For AI-company investments, report dependency concentration, data-rights findings, security events, accuracy testing, unit-economics sensitivity and changes to the company’s governance or regulatory posture. Escalate immediately when a material decision relied on an unreviewed output, confidential data was exposed, or a provider change invalidates a critical control.
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The decision rule
Approve an internal AI use when the office can identify an owner, control the data, test the output, require qualified review and continue operating if the tool fails. Consider an AI investment only after the company demonstrates lawful data access, clear IP ownership, manageable model dependency, credible security and accuracy controls, and defensibility beyond a temporary model advantage. If those facts cannot be evidenced, treat the uncertainty itself as a material risk rather than filling the gap with optimistic assumptions.
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