AI Is Reshaping Software. Is Your Security Strategy Keeping Up?

Software development is undergoing its biggest shift since the rise of cloud and DevOps. The difference this time? The shift is being driven by artificial intelligence, and it’s moving fast.

AI-powered coding tools have rapidly made their way into developer workflows. Agents and LLMs are helping teams move faster, automate more, and build in entirely new ways. But speed often comes with tradeoffs. Traditional security tools and processes weren’t built for this new reality — and that’s leaving organizations exposed.

If you’re starting to adopt AI to boost developer productivity, you’re not alone. But if you’re still relying on the same security frameworks you used before AI, it’s time for a serious rethink.

What is AI TrustOps?

AI TrustOps is a new readiness model designed to help organizations build and secure software in the age of AI.

It’s not a replacement for DevSecOps — it’s the natural evolution of it. AI TrustOps recognizes that the shape of software is changing. Developers aren’t just writing code anymore. They’re collaborating with AI assistants, integrating models, and deploying agents that make decisions on their own.

These changes bring huge opportunities for innovation, but they also introduce new types of risk. Software is being written faster than ever, often by tools that don’t understand the full context of what they’re creating. AI-generated code can look clean and correct on the surface, but still contain serious security flaws. And as AI becomes more tightly integrated into applications, the systems themselves become more dynamic, more complex, and harder to secure with traditional tools.

AI TrustOps gives organizations a way to think ahead. It offers a structured, practical approach to building security and trust into AI-driven development, without slowing innovation down.

Why now?

The AI adoption curve is steep, and most organizations are already well on their way.

The problem? Security often lags behind. LLM-generated code has a significantly higher vulnerability rate. AI tools are being used by teams across the organization — not just developers. And AI agents are starting to talk to each other, exposing entirely new interaction surfaces that weren’t there before.

All of this is happening before most companies have formal policies or processes in place. Shadow AI projects are popping up. Compliance guidance is still evolving. And many security teams are left scrambling to keep up.

We’ve seen this pattern before. When DevOps and cloud first hit the mainstream, security teams were often caught flat-footed. The result was a wave of new tools, processes, and cultural shifts — what eventually became known as DevSecOps.

AI is creating a similar moment. But the pace is faster, and the stakes are higher.

You may not be AI-ready if:

• You’re reviewing AI projects after they’ve already shipped.

• You can’t track what AI-generated code or models are being used.

• Your developers are using AI tools, but your security program hasn’t caught up.

• You haven’t defined who owns AI risk.

Even mature security teams are feeling the strain. The shift from deterministic, code-based systems to probabilistic, model-driven systems requires a new way of thinking. It’s not just about “where the bugs are” — it’s about how systems learn, evolve, and interact in unpredictable ways.

What it takes to be ready

AI software development doesn’t just introduce new tools.  It changes how software is created, who creates it, and what needs to be secured.

For the first time, people without a traditional development background can build functional applications by simply interacting with AI in natural language. This democratization of software creation brings massive gains in agility, but it also means the security boundary is expanding fast. AI-accelerated coding has made it possible for people who aren’t trained in secure coding to participate in the SDLC.

At the same time, machine learning models are being trained, fine-tuned, and deployed alongside conventional code, introducing risk factors that most AppSec tooling doesn’t account for. Things like model drift, data provenance, and prompt injection weren’t part of the conversation a few years ago — but they’re critical now as the AI-native space continues to grow..

Readiness means more than plugging in a few new scanners. It requires a clear strategy, cross-functional collaboration, and a culture that prioritizes safe, responsible AI adoption. It also means being proactive: looking beyond today’s vulnerabilities to the risks that are still emerging.

The good news? You don’t have to start from scratch. Much of the groundwork laid by DevSecOps — automation, shared responsibility, secure design — can be extended to AI use cases. But it takes intentional effort to build on that foundation in the right way.

The big shift? It’s not just about securing software. It’s about earning trust across your teams, your customers, and your systems.

Want a framework for moving forward?

We built one.

The AI Readiness Framework is a new model designed to help you assess where you are today — and what it takes to get AI-ready tomorrow. Whether you’re just beginning to explore AI-accelerated development or already deploying AI-native applications, the framework offers a structured path to help your teams build with confidence.

The framework identifies five key areas of focus — including governance, secure design, risk assurance, and culture — and explains why mature DevSecOps practices are essential before you even begin.

If you’re looking for a practical starting point, this is it. Download the AI TrustOps Ebook