Bringing AI into your business: the security questions to answer first

I put this together because of how often I was being asked the same question: we want to add AI to our workflow — what do we need to think about security-wise?

So here it is, written for the business rather than for the SOC.

What AI actually improves#

AI-powered tools can detect threats faster and more accurately than traditional methods, because they can analyse enormous datasets in real time and pick out anomalies that a person scanning dashboards would not.

That is not a vague claim, and it is worth quantifying:

A hundred days is the difference between an incident and a catastrophe. That is the upside.

Now the conditions attached to it.

The five considerations#

1. Data privacy compliance#

Your AI workflow has to comply with the regulations you are already subject to — GDPR, CCPA and whatever else applies in your markets.

Mishandling customer data inside an AI pipeline carries the same legal and financial penalties as mishandling it anywhere else. The model being new is not a defence.

2. Bias in the models#

Make sure your algorithms are transparent and free of biases that would produce unfair or inaccurate decisions.

This is a security concern as much as an ethical one: a model that systematically misjudges a category of user is a model producing wrong answers, and wrong answers erode customer trust quickly.

3. Data protection#

Encrypt customer data in transit and at rest when it passes through AI systems, and use secure APIs so the pipeline itself is not the weak point.

An AI integration usually means data moving to somewhere it did not go before. Every one of those new hops needs the same protection as the original store.

4. Model security#

Protect the models themselves from adversarial attacks — where an attacker crafts input specifically to manipulate the output and exploit weaknesses in how the model predicts.

This one gets forgotten. The model is an asset, and it is attackable.

5. Vendor security#

Evaluate third-party AI vendors for their security practices before you integrate. Every external solution you plug in inherits its risk into your environment.

The practical tip#

If you are adopting AI for workflows that touch customer data, put a governance framework around it and audit those systems regularly for compliance and security vulnerabilities.

Not once at launch. Regularly.

What did we learn?#

1. AI genuinely cuts breach lifecycle time — roughly 100 days, per IBM's data.
2. Regulation applies to AI pipelines exactly as it does anywhere else.
3. Biased models produce wrong answers, and wrong answers cost trust.
4. New data paths need encryption and secure APIs.
5. Models are assets and can be attacked directly.
6. Your vendor's security posture becomes yours.
7. Govern it and audit it on a schedule.

Source: IBM Security (2023), Cost of a Data Breach Report.

If you are working through this for your own business and want to talk it through, get in touch — this is the kind of question I enjoy.