TJE Ventures

The Risks Are Real and Manageable

AI risks are not hypothetical. Data has already been inadvertently exposed through AI tools. Decisions have been made on AI-generated content that turned out to be wrong. Biased outputs have produced discriminatory results in high-stakes contexts. None of this means AI should be avoided --it means it should be used with clear policies and good habits, just like any powerful business tool.

Data Privacy and Confidentiality

Do not share sensitive data with public AI services

When you type text into a public AI tool like ChatGPT or Claude’s consumer interface, that input may be used to improve the model, retained in logs, or accessible to the service provider. This means you should never enter: client names or personal information, proprietary business strategy, financial data not already public, employee personal information, health information, or anything covered by an NDA.

Enterprise versions of many AI tools offer data privacy protections that consumer versions do not --including opt-outs from training data use and contractual data handling commitments. If your organization uses AI at scale, the enterprise tier is worth the cost for the privacy protections alone.

Know what your AI vendor does with your data

Read the data processing terms for any AI tool your organization adopts. Key questions: Is your data used to train the model? Who can access it? How long is it retained? What happens if there is a breach? These are standard vendor risk management questions, and AI vendors should be held to the same standard as any other software vendor handling your data.

Understand regulations that apply to your industry

Healthcare organizations face HIPAA constraints on what patient data can be shared with AI systems. Financial services firms face SEC, FINRA, and other regulatory considerations. Legal professionals must consider privilege and confidentiality. If you operate in a regulated industry, consult your compliance team before deploying AI tools that touch regulated data.

Output Quality and Accuracy

Always verify before you act

AI can produce incorrect information confidently and without any visible warning sign. Any AI-generated content used in a consequential decision, customer communication, legal document, financial report, or published material should be verified by a human with appropriate knowledge. This is especially true for specific facts, figures, dates, citations, and legal or medical information.

Do not use AI output as a primary source

AI synthesizes from its training data, which has a knowledge cutoff date and may contain errors or biases from its sources. It is a starting point, not a source. Treat it like a knowledgeable colleague who hasn’t slept in a while and occasionally makes things up --useful for a first draft or a quick orientation, but not for citing in a document that matters.

Fairness and Bias

Test for bias in high-stakes applications

If your organization is using AI to assist in hiring decisions, loan approvals, pricing, healthcare triage, or any other decision with significant impact on individuals, you have an obligation to test whether the AI is producing biased outcomes across demographic groups. This is not optional ethics --in many jurisdictions it is a legal requirement. Build testing and auditing into your AI deployment process.

Do not assume AI is neutral because it is software

Software reflects the choices and data that went into it. AI reflects the biases in its training data --which is human-generated and therefore not neutral. Treating AI output as objective simply because a machine produced it is a significant and common error.

Intellectual Property and Copyright

Understand the IP landscape before publishing AI-generated content

The legal framework around AI-generated content is actively evolving. Key areas of uncertainty include: whether AI-generated images or text can be copyrighted, whether AI training on copyrighted material creates liability, and who owns output generated using a company’s proprietary data fed into an AI. Consult legal counsel before making significant commercial commitments around AI-generated content.

Be transparent about AI use where appropriate

Some professional contexts --journalism, academic writing, certain client relationships --have disclosure expectations or requirements around AI use. Know what applies in your field and err on the side of transparency.

Organizational and Process Risks

Establish an organizational AI policy

Many employees are already using consumer AI tools for work tasks, whether or not their organization has sanctioned it. This creates data and liability risks that policy can address. An AI policy does not need to be restrictive --it should clarify what tools are approved, what data can and cannot be entered, what review process applies to AI-generated content, and where AI should not be used without explicit authorization.

Maintain human accountability for AI-assisted decisions

When an AI-assisted process produces a bad outcome, the accountability still lies with the humans who deployed it and acted on its output. “The AI told me to” is not a legal or professional defense. Ensure that every consequential AI-assisted decision has a named human who reviewed it and is accountable for it.

Guard against over-reliance

There is a real risk that teams defer too readily to AI output because it sounds confident and authoritative. Build a culture of critical engagement: encourage people to question AI output, verify claims, and push back when something seems off. AI works best when it is one input into a thoughtful human process, not the end of one.

A Practical Checklist

• No confidential, personal, or regulated data in consumer AI tools
• Enterprise AI agreements reviewed for data handling terms
• All AI output verified before consequential use
• High-stakes AI applications tested for bias
• IP and disclosure questions reviewed with legal counsel
• Organizational AI use policy documented and communicated
• Human accountability assigned for every AI-assisted decision

“Using AI responsibly is not about limiting its use --it’s about using it in ways you can stand behind.”

← Previous Next: Hosting Your Own AI →