Insights from a CultureAI + TEISS roundtable on the realities of AI risk.

Executives everywhere are under pressure to deploy AI fast — but our recent 

, hosted by TEISS, revealed a growing concern: 

, and organisations are taking on more risk than they realise.

While most enterprises have mature technical controls, many are missing visibility into 

. Employees are integrating unsanctioned tools, experimenting with automation, and feeding sensitive data into SaaS faster than governance frameworks can adapt.

The TEISS Breakfast Briefing at London’s Goring Hotel brought together senior leaders from finance, legal, professional services, and tech to explore a critical question:

How do we enable AI innovation without losing control of data, compliance, and trust in the process?

AI Adoption Is Outpacing Governance — and Controls Can’t Keep Up

 has overtaken policy, governance, and technical oversight. One leader described how confidential HR data had already been uploaded to AI platforms before safeguards were in place. "We had to move fast to block services and retrofit controls later,” they said.

Even in more advanced organisations, there’s still an education gap. “We had to teach employees what they could and couldn’t share with AI,” another attendee explained. “They didn’t know.”

 and embedded SaaS features is further complicating visibility. “It’s not a human, but it’s not a static machine either,” one participant noted — highlighting how traditional governance models were never designed for adaptive, decision-making systems.

executive ambition and operational assurance

AI Risk Management Needs Visibility, Transparency, and Context

Across both the public and private sectors, the pressure to “do more with less” is driving rapid AI deployment — often without the security and governance maturity to support it.

“There’s a rush to bolt AI onto SaaS products,” Moore observed. “But many vendors haven’t considered security.” This leaves security teams in a constant race to 

The risk isn’t just exposure — it’s opacity. One bank discovered its AI-driven fraud model was disproportionately rejecting applications from people with South Asian names. The issue traced back to biased training data that included historical fraud cases linked to the Tamil Tigers. Over time, the model learned a 

false correlation between name patterns and fraud risk

visibility, explainability, and intent awareness

 users behave the way they do — not just monitor which tools are in use.

From Training to Teaching — At the Moment of Risk

Every attendee agreed that traditional awareness training won’t close the gap. “It can’t be one and done,” said one security leader. “

It has to be constant. And it has to be paired with other controls

We know about hallucinations and false confidence,” said one executive, “but people forget in the moment. They see an answer and stop thinking critically.”

: introducing “friction” into risky workflows, with a message telling users they can’t proceed or asking them to confirm their intentions.

Rather than relying on static education, organisations are moving toward 

intent-aware, behaviour-based interventions

 — detecting risky actions as they happen and turning them into teachable moments. 

 is fast becoming a foundation of secure AI enablement.

Executives also highlighted emerging challenges that extend beyond compliance — from 

. “Someone will always try to make an LLM reveal confidential data,” an attendee warned. “Not out of malice — just curiosity.”

 rising, that curiosity can quickly become compromise. “Desperate people are more likely to sell data,” said another. “It’s a growing concern.”

 isn’t just about blocking tools — it’s about ensuring every use of AI is 

measurable, compliant, and human-friendly

Those at the briefing were in agreement: 

organisations can’t slow down AI innovation — but they must make it safer, more transparent, and more accountable.

The enterprises leading AI security today are shifting from control to enablement. They’re building systems that:

Whether the threat comes from a rushed deployment, a careless insider, or an attacker exploiting behaviour — the key is visibility, context, and control. AI adoption should never come at the cost of confidence.

Takeaway: Safe AI Adoption Starts With Visibility and Control

 is unstoppable — but without full visibility into how AI tools are being used and the intent behind that use, most enterprises are managing risk reactively.

measurable, secure, compliant, and human-friendly

 — giving organisations the intelligence to empower people safely, not restrict them.

To learn how leading organisations are enabling secure AI adoption without slowing innovation, explore 

 — which gives security and compliance teams the visibility, controls, and coaching tools to manage AI usage without killing productivity.

The TEISS Breakfast Briefing at London’s Goring Hotel brought together senior leaders from finance, legal, professional services, and tech to explore a critical question: How do we enable AI innovation without losing control of data, compliance, and trust in the process?

AI Adoption Is Outpacing Governance: Conversations on Managing AI Risk

Recommended for you

The AI Hunger Games: The Rapid Adoption of DeepSeek: A Security Nightmare

Securing AI in the Enterprise: Facilitating Innovation whilst Securing Adoption

The Back Room Problem: Why Most Organisations Lack AI Data Visibility