Evolving Security Champion Programs: Leveraging AI for Enhanced Governance and Collaboration

| Editorial Team On 25 Sep, 2026

The Security Champion Playbook Is Evolving — And AI Is Driving the Change

Security champion programs built around embedding security into engineering teams are facing a pivotal transformation as artificial intelligence reshapes how software is built and how organizations operate.

The question facing security leaders in 2026 is no longer whether champion programs matter. It is whether those programs are equipped for what comes next.

The Mission Is Expanding Beyond Secure Code

Security champion programs were designed to solve a deceptively hard problem. Policies and annual training modules rarely change behavior. But a developer talking to other developers — someone who understands the pressure of a sprint deadline or the friction of a pull request review — can move the needle in ways that centralized security teams simply cannot.

That peer-based model remains as relevant as ever. What is changing is the scope of the conversations champions need to have.

For years the core questions were straightforward: How do we write more secure code? How do we identify common vulnerabilities? How do we get developers invested in security outcomes? Those questions have not disappeared. But AI adoption has added an entirely new layer of complexity to the conversation.

Today's champions are increasingly being asked to weigh in on where AI should be used in the development lifecycle, what developers need to review when AI generates code, and how teams should handle AI-generated vulnerabilities or insecure patterns. Perhaps most critically, champions are being called on to translate abstract AI governance policies into something a developer can actually act on when they open a pull request.

No policy document answers those questions. Only people embedded in the work can.

The Gap Between Policy and Practice

This gap between written policy and lived practice is where champion programs prove their worth. Abstract governance frameworks mean little if the people doing the work cannot interpret them in real time. Champions bridge that gap not through authority, but through proximity — they sit inside the teams, the workflows, and the decisions where security either happens or it doesn't.

As application security strategy continues to evolve, organizations that rely solely on centralized policy enforcement will find themselves perpetually behind. Those with distributed human networks already embedded in the work will adapt faster.

Why AI Makes Human Judgment More — Not Less — Important

There is a tempting assumption that AI tools will eventually reduce the need for human security advocates. The evidence points in the opposite direction.

AI does not eliminate the need for judgment in context. It amplifies the need for trusted people who can apply that judgment where the work is actually happening. An established champion network already has the relationships across engineering teams to help adapt existing security practices to AI-enabled workflows.

But the opportunity extends well beyond engineering. As AI moves into product development, data science, legal, compliance, HR, and executive decision-making, organizations face a much broader challenge. Who are the trusted people who can help every team — not just developers — adopt AI responsibly?

The answer is unlikely to be another centralized security function. Centralized teams are already being stretched thin governing new tools, new workflows, and new risk categories without becoming a bottleneck. The more scalable answer may be a distributed network of advocates who are already embedded in the business.

A useful parallel: the security champion model applied at organizational scale — less like a dedicated security task force and more like a distributed network of trusted individuals working from within to shift culture from the inside out.

The Human Element AI Cannot Replace

AI tools can flag a vulnerable dependency or surface a misconfigured access policy. What they cannot do is read the room in a sprint retrospective, push back constructively on a rushed deployment decision, or build the kind of trust that makes a developer actually change how they work. Those outcomes require human relationships — and champion programs are, at their core, relationship infrastructure.

The organizations that will navigate AI adoption most effectively are not those with the most sophisticated tooling. They are the ones with the most capable people networks. Building and sustaining that kind of long-term cybersecurity culture across teams and departments is not an overnight effort — it is the compounding result of deliberate, sustained investment in people.

For further context on how distributed human networks compare to centralized governance in managing emerging technology risks, the NIST Cybersecurity Framework provides a widely adopted reference point for structuring organizational security responsibilities.

From Security Champions to AI Champions

Building on What Already Exists

Organizations that have already invested in champion programs hold a significant structural advantage. They have something that cannot be stood up overnight: a community of people who understand security, influence their peers, and know how to translate requirements into real-world practice.

The next step is figuring out how to extend that model. Some organizations may evolve their existing security champions into AI-focused advocates. Others may build a broader network that draws from engineering, product, data, legal, compliance, and leadership functions. The structure will vary. The underlying principle stays the same.

Do not make security responsible for changing everyone's behavior. Build a network that helps everyone participate in that change.

Diagnosing Where Your Program Stands

For organizations evaluating where their current champion programs stand, several diagnostic questions are worth asking now:

Has the program begun addressing AI adoption in development workflows, or is it still focused exclusively on traditional AppSec topics?
Are champions genuinely influencing behavior, or is the program measuring attendance and activity as a proxy for impact?
Can champions translate a responsible AI policy into something actionable at the pull request level?
Does the champion network reach beyond engineering to cover every function where AI is now being adopted?

The strongest programs are not simply communities. They are mechanisms for changing how people work.

Extending Champion Influence Across the Organization

One dimension that many programs underinvest in is the human side of security awareness across non-technical functions. Improving employee cybersecurity awareness across the wider organization becomes significantly more achievable when there are trusted internal advocates in every department — not just in engineering. AI adoption makes this cross-functional reach not just valuable, but necessary.

What an AI Champion Actually Does

The role of an AI champion is not to be an AI expert. It is to be a trusted, embedded guide who helps their team ask the right questions, apply organizational policy in context, and flag concerns before they become incidents. In practical terms, that might look like:

Helping a product team understand what responsible AI use looks like during ideation and prototyping
Reviewing AI-generated outputs with a critical eye before they enter production
Escalating emerging risk patterns to centralized security or governance teams
Keeping AI governance from feeling like an abstract compliance exercise

What Comes Next for Security Champion Programs

The security champion model is not going away. If anything, it is becoming the most practical answer to a much larger organizational challenge — how to build security into everyday work when the way everyone works is changing simultaneously.

AI is accelerating that shift faster than most governance frameworks can keep pace. Organizations that adapt their champion programs now will be better positioned to turn existing networks into trusted communities capable of helping every team navigate new technology and translate security intent into daily action.

On October 15 and 16, Security Journey is hosting the Security Champions Summit 2026, a free fully virtual event for champions and program leaders. Day one focuses on building, scaling, engaging, and measuring champion programs. Day two explores what the champion model looks like when applied to AI adoption, including how to build AI champion communities across the organization. Seats are limited and registration is free.

How Readers Can Use This Information

Program leaders can use the diagnostic questions outlined above to assess whether their current champion program is equipped to address AI adoption — and identify specific gaps to close before AI-related incidents surface.
Security professionals can make the case for expanding champion programs beyond engineering by framing it as an organizational AI governance strategy rather than a purely technical security initiative.
Developers and non-security employees who already serve as informal security advocates can position themselves as candidates for expanded AI champion roles by building fluency in responsible AI adoption practices within their specific functions.