F5 Networks turns 30 this year as a $3 billion public company serving more than 80% of the Fortune 500. CEO François Locoh-Donou, speaking on the GeekWire Podcast, detailed the company's latest reinvention - expanding into AI security - and announced the acquisition of SurePath AI this week. With 6,500 employees and a customer base that includes most of the world's largest enterprises, F5 now aims to address a growing problem: organizations have little visibility into how AI agents and models interact with their systems.
From video games to internet infrastructure
F5 started as a group of University of Washington students building online video games before pivoting to load balancing in the 1990s. Locoh-Donou said the move was painful but common among tech survivors. "It's not uncommon for technology companies to make a pretty substantial pivot in the early days. It's a tough decision, but a lot of the successful companies we look at today had to make big pivots. In the case of F5, it was video games to load balancing - which is not an obvious one, but it paid off in big ways."
The enterprise AI visibility gap
As enterprises deploy more AI, they lose sight of how the technology operates across their environment. Locoh-Donou described the challenge directly: "The more an enterprise adopts AI, the less visibility it has into what AI is crawling in the organization. It deploys more agents, and these agents call on tools; it deploys more models, and those models integrate with applications and fetch data in different places. Understanding which employee is using what AI, and what agent is using what tool, is quite complicated."
F5's response is a consolidation play. Instead of stitching together multiple point tools, the company is building a platform that bundles discovery, governance, testing, and protective guardrails. "Having four, five, six different tools to discover, test and secure your AI is a nightmare. So we're building an AI security platform that includes all of these capabilities - discovery of AI models or agents, the governance and visibility around them, testing of these models, and the guardrails to protect them," Locoh-Donou said.
For executives evaluating such platform strategies in their own organizations, AI for Executives & Strategy provides ongoing coverage of enterprise AI infrastructure moves.
Leadership built on self-belief
Locoh-Donou's leadership philosophy rejects command-and-control pressure. "I don't believe much in what I call north-south pressure, the idea that you create a high-performance team by a boss putting pressure on their subordinates. I believe you create a high-performance team first by attracting the best possible talent, then instilling self-belief in each of these people, and letting the pressure come from themselves and from their peers," he said.
That perspective stems partly from his own early career experience as a Black executive from Togo working in technology. "When I started in the technology industry, I didn't think I belonged, let alone becoming a CEO, because I looked at the people around me and there was no one who looked like me. In the first few months of my first job, my hope was to not get fired. That was the dream. And it was quite lonely."
That history shaped the message he delivered to a group of high school students from underrepresented backgrounds who visited F5 Tower before attending a World Cup match. "It was important for me that they hear from a Black executive in this technology industry that the technology industry is also for them, and that they have a place here - even if they're not coding on computers every day, even if they have no parent or sibling or anybody in their family who's ever set foot in a technology company. And also that their voice matters."
Why this matters for executives and strategy
Enterprises accelerating AI adoption risk fragmentation that creates security and governance blind spots. F5's move to integrate AI discovery, testing, and guardrails into a single platform reflects a market shift toward consolidated tooling. For leaders steering AI strategy, the lesson is clear: visibility into how models and agents access data is not a compliance afterthought - it's a core architectural requirement that can define whether AI deployments scale safely.
Your membership also unlocks:
