Talk. Transcribe. Turn conversations into security context.

What the problem is

Most security teams do not operate on a clear picture of what they actually need to secure.

Modern, tech-enabled companies are messy: many teams, each with their own tech stack, SaaS tools, automations, and processes.

It is often unclear who does what, how teams work together, where technical interfaces between services and applications are, who can access what, and which data flows run through which systems.

Add external providers, integrations, and a layer of non-trivial business logic on top, and the whole thing becomes hard to grasp from the outside.

Existing documentation barely helps. It is incomplete, outdated, written from the wrong angle, or just too dense to be useful.

On top of that, the business keeps moving: products evolve, new services appear, and teams are restructured.

From a security perspective, this means it is genuinely hard to identify concrete security problems, set priorities, or derive a sensible roadmap.

You can only secure what you know exists.

The flip side is just as important: if a CISO has real clarity about how the business is built, what management's risk appetite is, and where the company is heading, the job becomes much simpler. You "just" have to prioritize and execute targeted controls, whether technical or process-oriented.

How AI can help

The way out is not to stare at tools harder. It is to systematically harvest human context and make it usable with AI.

That starts in a very practical way: spend a few days or weeks having 30-60 minute conversations with department heads, team leads, and other key people, and ask concrete questions about their scope:

• Where does your responsibility start and end?
• What does your team actually do all day?
• Which tools and SaaS platforms do you rely on?
• What are your key internal and external processes?

Let people talk. Make them go broad and deep. Record everything.

Today, transcription is trivial.

Once you have transcripts, feed them into an LLM, individually or in larger batches, and use it to turn raw conversation into structured understanding.

You can generate clean descriptions for documentation, ask what risks and weaknesses were implicitly surfaced in a session, or come back later and ask: given everything this team told us, how could they be compromised?

You can enrich this further by having people show their actual tools, the CRM, support queue, or login flows, while capturing and summarizing what happens there.

And that is before you even connect code repositories or internal knowledge bases.

The combination of targeted conversations, transcription, and an LLM as analysis layer is already enough to build a much sharper, living picture of your business and technical environment.

Once that exists, the original problem, "we do not really understand our environment and its unique risks," is largely gone.