The financial services industry is currently navigating a high-stakes paradox. On one hand, there is an intense, board-mandated race to accelerate AI adoption to secure competitive advantages. On the other, this velocity is colliding with a foundation of legacy infrastructure and fragmented security and creating an environment where the speed of innovation is starting to outpace the maturity of governance.
At Netskope, we recently surveyed 100 North American IT leaders in financial services to measure momentum in AI adoption. The findings reveal a landscape of aggressive adoption meeting significant operational friction. The central question for every CISO and CIO in the sector is no longer “Should we use AI?” but rather: “How do we govern this innovation before it derails our security?”
And the industry’s approach to this challenge is diverse. According to our survey, 62% of organizations have adopted managed, third-party AI applications for rapid deployment, while 54% are building private, internal AI applications to maintain stricter control over data and IP. Notably, 26% are already experimenting with autonomous AI agents. These approaches are not mutually exclusive; most organizations are layering these strategies to balance agility with governance.
However, the fundamental challenge is execution: only 1 in 100 of these organizations believe that they are able to accelerate AI adoption without compromising security or operational integrity. This statistic underscores a clear reality where speed and control are currently misaligned.
Navigating the Potholes of AI Innovation
Our research highlights three primary “potholes” hindering progress. The first is shadow AI (employees using consumer AI tools the organization hasn’t approved) which worries 56% of IT leaders, largely because of the risk of sensitive financial data leaking into public models without anyone noticing until it’s too late. Data sprawl and the compliance risk that comes with it is concerning 43%. And 53% say they’re afraid of losing visibility into how AI is actually making decisions, which matters a great deal when regulators expect an explanation for every automated call a bank makes.
Infrastructure serves as the primary barrier to addressing these concerns. 60% of leaders cite legacy architecture as the main roadblock to new AI product launches, outpacing policy or budget constraints. While 31% cite insufficient investment and 21% point to fragmented, siloed security tools. The common thread is the need for an architectural shift.
None of this means slow down. It means the security architecture underneath AI adoption needs to catch up to the pace of the adoption itself.
Securing the AI Ecosystem
Closing the gap requires shifting from a model where speed and security are viewed as opposing forces, toward one where visibility is integrated by design. A zero trust approach (where every user, device, and AI application is verified continuously) provides the real-time visibility that security teams need to be able to govern the entire AI ecosystem.
Organizations that treat AI governance as an extension of their existing security stack, rather than a separate initiative, are the ones successfully scaling. Bolting on standalone AI monitoring tools to disconnected point products merely adds blind spots. Conversely, folding AI visibility into an existing control plane allows security teams to treat AI activity with the same discipline as regular network traffic.
When security acts as an enabler rather than a gatekeeper, the business outcomes are tangible:
- Accelerated time-to-market for new products and services
- Enhanced network performance for hybrid workforce support
- Improved data security and compliance posture
- Increased organizational confidence to greenlight further AI investments
Securing AI at Netskope
The Netskope platform was built to resolve this tension, providing inline visibility into AI activity (whether managed or unmanaged) without forcing a trade-off between speed and control. Netskope One AI Guardrails apply real-time policy to AI usage, while our Zero Trust Engine extends continuous verification to every agent and application on the network.
If your organization is navigating the transition to the AI era, our AI Risk and Readiness Report provides deeper insights into adoption maturity and governance strategies. For a visual summary of the findings, you can also refer to our “The Road to AI: The Financial Sector Needs to Avoid Potholes” infographic.
