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Contents
Published industry surveys repeatedly find that a large share of employees using generative AI are doing so through personal accounts - not company-sanctioned platforms. Personal credentials. Company data. No audit trail. This is shadow AI, and it is already happening inside pharmaceutical commercial operations at a scale most organisations have not measured and would rather not see.
What shadow AI looks like in practice
A rep is preparing for a key account meeting. They need talking points tailored to the customer's situation. So they open ChatGPT on their phone - personal account - and paste in the customer's purchasing history, competitive positioning, and strategic challenges.
The AI generates talking points.
Also: company data left the building. It is now on servers the company does not control, subject to terms of service the company never agreed to, potentially used to train models that competitors might benefit from.
Nobody authorised this. Nobody tracked it. Nobody knows it happened.
That is shadow AI. And it is not an edge case.
Why existing controls do not catch it
Most organisations have data security policies. Access controls, encryption requirements, approved vendor lists. Those policies were designed for a world where data stayed inside systems the organisation controlled.
Generative AI broke that assumption. A rep can now extract data, pass it through an external AI, and get a result - without triggering a security alert.
The governance vacuum is not a failure of intent. It is a failure of pace. Policy and tooling have not kept up with the speed at which employees adopted AI.
The pharmaceutical risk profile
In pharmaceutical commercial operations, the data at risk is not generic.
Customer intelligence. Prescriber relationships, prescribing patterns, competitive positioning. If it surfaces outside your control, the damage is not recoverable by policy after the fact.
Pricing information. Discounting strategies, tender responses, margin structures. Competitively devastating if exposed.
Patient-adjacent data. Call reports and customer records sometimes contain information that sits close to patient data. In that context, regulatory exposure compounds commercial risk.
A rep who pastes customer records into a personal AI account is not trying to create a compliance incident. They are trying to do their job. That is what makes shadow AI hard to govern with prohibition alone.
Breaches involving shadow IT - including shadow AI - are consistently found to cost organisations significantly more than breaches within monitored systems. Shadow systems take longer to detect, exposures are harder to scope, and remediation is more complex because no one knows what went where.
What governance actually requires
Visibility first. You cannot govern what you cannot see. Before policy, organisations need to understand the extent of shadow AI usage - anonymous surveys, network monitoring, honest conversations. The data will be uncomfortable. That discomfort is the point.
Sanctioned alternatives. Employees use shadow AI because it helps them work. Prohibition without alternative does not eliminate the behaviour; it drives it underground and removes any visibility. Provide approved AI tools with guardrails.
Specific policies. What data can be used with AI tools? What cannot? Vague guidance produces inconsistent behaviour. Specific, written rules produce compliance - and a defensible position if something goes wrong.
Technical controls. Where feasible, implement data loss prevention systems configured for generative AI endpoints. These exist and can be deployed without prohibiting AI use.
Training that means something. Not checkbox compliance. Genuine education about why the rules exist and what is at stake. Employees who understand the risk behave differently from employees who have signed a policy they never read.
The uncomfortable conversation
Many organisations quietly avoid looking for shadow AI. Knowing creates obligation. Ignorance provides deniability.
This is dangerous. Regulators do not accept "we did not know" when the knowing was available. And the evidence is consistent: finding out during incident response is far more expensive than finding out now.
The conversation is uncomfortable. It will reveal behaviour that has been happening without oversight, and it will expose gaps in controls assumed sufficient.
But the alternative - waiting until a breach forces awareness - is worse. The question is whether you govern it proactively or discover it during incident response.
The organisations we work with that have the cleanest answer are the ones who did the uncomfortable audit first, built governed data environments, and gave their people AI tools they were allowed to use.
Written by
Dieter Herbst
CEO at Herbst Group. Working with pharmaceutical commercial leaders across South Africa, Kenya, and Brazil to transform sales force effectiveness through evidence-based approaches.
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