How I Use AI to Review Data Protection Agreements (and Why You Might Want To)

As the CEO of a software company, I spend a lot of my time thinking about risk, trust, and speed. Data Protection Agreements (DPAs), contracts that touch sensitive data, sit right at the intersection of all three.

They’re essential if you’re handling customer or employee data. They’re also dense, technical, and often written in a way that feels like someone took normal English and ran it through a legal thesaurus. I’ve been on both sides, drafting them as a lawyer, and now reviewing them as a CEO, and I can say with certainty: the devil is in the details.

So, I'm going to show you how I create consistency and speed by using legal software tools like Gavel Exec, our AI-powered redlining tool inside Microsoft Word. It’s not some generic chatbot that happens to know the word “GDPR.” It’s trained with feedback from practicing lawyers and designed to behave like a reliable associate.

You open the DPA in Word, and either ask the chat to edit or redline the document or run it through the DPA Playbook (a play-by-play of rules and preferences that you can customize). The AI flags what matters most, thoroughly and substantively. It’s like having someone read through the agreement many times, but without the billable hour clock ticking in the background.

Examples of Five Things AI Can Check in Your DPA

1. Data Processing Scope

• What it does: Gavel Exec compares the stated data processing purposes in the agreement to your intended business use, identifying when the scope is broader than necessary.
• Why it matters: Phrases like “any lawful purpose” might sound safe, but they give the other party broad leeway to use personal data in ways you never approved.
• Example AI action: Exec inserts a redlined revision narrowing the clause, e.g., replacing “any lawful purpose” with “solely for providing the Services described in Exhibit A.”

2. Subprocessor Requirements

• What it does: Reviews how the agreement addresses subcontractors who will process personal data on your behalf, and compares it to your Playbook rules.
• Why it matters: Privacy laws like GDPR require you to maintain visibility and control over all subprocessors.
• Example AI action: If the clause is vague, Exec inserts a tracked-change edit adding: “Processor shall provide at least 30 days’ written notice before engaging any new subprocessor, and Customer shall have the right to object on reasonable grounds.” This turns a passive requirement into an enforceable contractual right.

3. Cross-Border Data Transfers

• What it does: Scans for provisions covering transfers of personal data outside the EEA or other regulated regions, and checks for references to Standard Contractual Clauses (SCCs) or equivalent safeguards.
• Why it matters: Missing or outdated transfer mechanisms can halt operations or trigger regulatory penalties.
• Example AI action: Where the agreement is silent, Exec proposes an inserted clause referencing the latest EU SCCs and, if applicable, the UK International Data Transfer Addendum, ensuring legal compliance without requiring you to research the latest model clauses.

4. Security Measures

• What it does: Evaluates whether the security obligations are specific enough to be enforceable and aligned with your internal policies.
• Why it matters: “Appropriate security” is subjective; you need defined measures like encryption, access controls, and breach notification timelines.
• Example AI action: Exec inserts detailed requirements (e.g., “including but not limited to AES-256 encryption at rest, TLS 1.2 or higher in transit, and notification of any security incident within 48 hours”) right into the clause, letting you accept the language instantly.

5. Liability and Indemnification

• What it does: Identifies caps on liability and checks for carve-outs related to data breaches or confidentiality violations.
• Why it matters: The liability section often determines who bears the real financial risk if there’s a breach.
• Example AI action: If the clause caps all liability at total fees paid, Exec edits the language to carve out unlimited liability for intentional misconduct or gross negligence related to data breaches, so you can preserve your protection while keeping reasonable caps elsewhere.

Why I Review Our DPAs with AI

As the founder of a tech company, reviewing a third party DPA often meant hours of squinting at repetitive clauses, making sure the obligations lined up with my client’s reality. Now, I can’t afford to have me or our legal team bogged down in that kind of manual review, especially when speed can make or break a customer and where things could be missed.

AI doesn’t replace judgment. It doesn’t make the call on what risk you can live with. But it does surface the important parts faster, so your team can spend their time where it counts (deciding what to accept, what to push back on, and what’s a dealbreaker).

The way I see it, the real win isn’t just efficiency. It’s confidence. Confidence that when you sign a DPA, you’re not leaving a hidden landmine in the fine print. And for anyone responsible for protecting data, and the trust that comes with it, that’s worth its weight in gold.

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