Legacy systems, AI, and security: the new equation for 2026

For a long time, legacy systems were said to be doomed. Then the cloud was supposed to sweep everything away. Today, it’s artificial intelligence that concentrates all expectations, all fantasies… and sometimes all fears. Yet the reality on the ground is far more nuanced.

In 2026, CIOs are facing a new equation—more complex, but also richer: how can you integrate AI, strengthen security, and modernize critical systems without breaking what already works? The answer is not a headline-grabbing announcement. It relies on a pragmatic, progressive, and deeply hybrid approach.

Contrary to what some predicted only a few years ago, legacy is very much alive—and it will remain so. These applications carry the core of business operations: finance, logistics, production, billing, and compliance. These systems process massive volumes of data, with performance and reliability requirements that are rarely matched.

Legacy is not a problem in itself. The real problem is not knowing how to evolve it intelligently.

The true Achilles’ heel of legacy isn’t technical—it’s human. Business rules are buried in code. Documentation is often missing, incomplete, or outdated. And above all, the experts who master these critical applications are gradually retiring. This generational transition, announced for more than fifteen years, is accelerating sharply. Some companies are already in critical situations because they failed to anticipate knowledge transfer.

To address this, organizations want to centralize and structure knowledge about their application portfolio, so code becomes understandable, shareable, and usable by different profiles. That is precisely the goal of Application Intelligence platforms such as DISCOVER.

Yes—AI is a revolution. And that’s exactly why we must keep a cool head. Every technological revolution comes with side effects: job displacement, massive energy consumption, and a worrying carbon footprint. It’s time to stop using a sledgehammer to crack a nut. AI is powerful, but it must be used with discernment—based on real, controlled use cases.

Historically, critical systems rely on deterministic algorithms: you start from point A, you reach point B, with a guaranteed result. AI, by nature, is probabilistic. It explores possibilities, accelerates, suggests—but does not always guarantee the same result.

Opposing these two approaches makes no sense. Determinism guarantees; AI accelerates and explores. Combining both is what creates value.

One of AI’s biggest contributions is its ability to open development to profiles beyond historical experts. Until now, expertise was often held as a form of power. AI changes the game: it enables business teams to reclaim part of the Information System ecosystem, gain autonomy, and build new applications faster. The information system gradually becomes data-centric, with secure data platforms consumed by applications that are created faster—sometimes on the fly.

But AI is only relevant if it is well fed. Without knowledge of the existing system, without a metadata repository, it remains blind. Conversely, when enriched by decades of application analysis, it becomes genuinely useful. The point is not to inject AI everywhere, but to anchor it in existing knowledge, so it becomes relevant, reliable, and actionable. That’s the logic behind organizations using DISCOVER as a structuring repository, serving as a foundation for more relevant and more reliable AI use cases.

Companies have multiplied CI/CD pipelines, often without true global coherence. The result: exploding resource needs, even as DevOps skills remain scarce. This fragmentation shows its limits—especially in critical environments where stability and compliance are essential.

Platform Engineering is emerging as a compelling answer. The goal is to industrialize environments, pool skills, and standardize pipelines, while maintaining a high level of control. It is a deep evolution of DevOps, perfectly suited to critical systems and complex organizations. In this model, some organizations rely on central platforms capable of orchestrating deployments across heterogeneous environments (legacy, cloud, mainframe) such as DROPS , used as a point of convergence and control.

Security can no longer be treated as an end-of-chain activity. Static application security testing is becoming a standard, especially in legacy environments where vulnerabilities are harder to detect. In this context, specialized tools such as ARCAD CodeChecker are used to integrate cybersecurity and code quality directly into development processes.

Data leaks are multiplying, with very concrete consequences: targeted phishing, identity theft, and direct exposure of end users. CIOs and executive leadership bear responsibility. Continuing to underestimate this risk is no longer acceptable.

AI consumes huge amounts of data. But once exposed, that data can circulate far beyond the perimeter originally intended. The question is no longer only about storing and protecting data, but about controlling how it is used.

Anonymizing data makes it possible to reconcile innovation and compliance. It is an essential condition for using data in AI or Big Data contexts without exposing sensitive information. In this framework, solutions such as DOT Anonymizer help align innovation, compliance, and security.

In critical systems, the smallest error can have major financial consequences. Non-regression tests act as a true application firewall, securing every evolution. Without automated testing, it is unrealistic to claim you can modernize quickly and safely. That’s why tools such as ARCAD Verifier are used to drastically reduce operational risk.

DevSecOps does not mean abandoning historical best practices. Segregation of duties (development, testing, production release) remains fundamental. Code reviews, multiple validations, and change processes are indispensable guardrails.

The “all cloud” narrative has shown its limits. Economic, regulatory, and technical constraints make this approach unrealistic for many critical systems. Legacy, often on-premises, remains highly relevant.

The future is hybrid:

• robust, secure back ends,
• more agile digital front ends,
• controlled coexistence of cloud and on-premises.

This is not a failure—it is a rational choice.

In 2026, the real challenge is not to innovate faster, but to innovate without weakening what already exists. Modernization cannot be a big bang. It must be progressive, controlled, and secure. The goal is not to replace everything, but to intelligently evolve systems that are, by definition, critical.

Artificial intelligence has a key role to play—provided we move beyond fantasy. AI is not a magic wand. Deployed without governance, it becomes a risk. Implemented in a controlled way, it becomes an accelerator. The same logic applies to infrastructure: “all cloud” is not an end in itself. Hybrid is now the norm.

Ultimately, security is not a brake on innovation, legacy is not a problem to eliminate, but a foundation to build on. CIO success in 2026 will rely on a pragmatic, hybrid, and responsible approach—where every evolution is designed as one more step forward, not a rupture.