Welcome to Legal Prompting, the podcast dedicated to legal methods in the age of AI.
I'm Nicola Fabiano and this is episode 7.
In the previous episode, we saw how to use AI to analyze contracts and clauses, spot
weaknesses, and compare versions.
Now we take a step further.
Those prompts do not live in isolation, they live inside business processes, inside compliance
workflows that involve people, documents, deadlines, and responsibilities.
Today we talk about Legal Prompting in Corporate Compliance Workflows.
Compliance is a broad word.
It includes privacy, anti-corruption, anti-money laundering, information security, organizational
liability models, and internal controls.
In each of these areas, AI can help, but it can also introduce new risks if it is added
without criteria.
Let's start from a principle.
AI is not a neutral tool that adds to an existing process.
AI changes the process.
It changes who does what, how decisions are documented, and who is responsible for outcomes.
Before writing a prompt, we need to ask where it fits into the workflow and what changes
it entails compared to the way things were done before.
Let's see three concrete applications.
First application, handling data subject requests under the GDPR, a controller receives requests
for access, erasure, and portability.
AI can help with the first classification of the request, the extraction of relevant
information, and the preliminary check of deadlines.
The prompt must specify the legal framework, the requester's role, and the type of request.
The AI's response is a technical draft, the decision remains with the responsible person.
Second application, the periodic review of corporate policies, codes of conduct, privacy
policies, and internal procedures.
AI can compare the current version with updated legal references and flag inconsistencies,
gaps, and obsolete references.
The prompt must indicate the reference standards and the scope of the review.
The output is a map of issues, not a new version of the document.
Third application, monitoring of internal reports and whistleblowing.
AI can help with initial triage of reports and categorization by risk type.
Here, caution is at its highest.
Reports contain sensitive data, protected identities, and facts that may become the
subject of investigations.
The infrastructure must guarantee confidentiality, traceability, and data segregation.
It is not a question of prompt, it is a question of governance.
From these applications, a working rule emerges.
Every use of AI in compliance must be documented.
Which prompt was used, on which model, by which operator, with which outcome.
Without this documentation, compliance cannot be verified.
And a process that cannot be verified is not compliant whatever the apparent result.
There is another point.
AI introduces a new operational risk, the risk of automating error.
If a prompt is imprecise, every use of that prompt will produce an imprecise outcome.
The scale of the error grows with the scale of use.
That is why corporate prompts must be treated as operational tools.
They must be versioned, tested, validated, and updated as we do with any procedure.
And then there is responsibility.
AI is not accountable for anything.
The controller, the employer, the professional, and the consultant are accountable.
Human oversight is not a formal detail.
It is the only way to bring decisions back to actors who can answer for them.
In the next episode, we will enter a delicate territory.
Professional secrecy and the choice of AI infrastructure.
Which models can be used, which cannot, and why the choice of infrastructure is already a compliance decision.
To stay updated, subscribe to the newsletter at nickfab.eu
Thank you for listening. See you next time.