Legacy Code Might Be Holding Your Enterprise Back

Your enterprise might need to look under the hood. 

A recent report from Saritasa found that 62% of American companies are running outdated software. About 43% of those surveyed said legacy code represents a major risk. 

In banking and finance, for example, 95% of ATM activity in the US and 80% of in-person credit card transactions still run on code that’s 60 years old. Financial services are at the forefront of code modernization, driven by legacy footprints, regulatory pressures and the risks involved in operating old code, said Dr. Ranjit Tinaikar, CEO of Ness Digital Engineering. 

“Healthcare, travel, hospitality, retail and manufacturing are not far behind the first wave,” he added. 

And the market for updating legacy tech could be lucrative, Tinaikar noted, with services in application modernization, mainframe modernization and more potentially valued at more than $100 billion combined by 2030. 

As AI adoption starts to take hold, stakeholders are looking to resolve the technical debt of legacy systems, reduce security risks and unlock new capabilities, Tinaikar said. Plus, “modernizing outdated platforms is the only way to fully tap into AI-enabled productivity gains,” he noted. 

So how can enterprises brush the dust off their aging code? One solution may be intelligent engineering, said Tinaikar, or optimized development teams that leverage state-of-the-art technologies, including AI, to modernize legacy systems. 

• Instead of limiting projects by costs or spending on large engineering headcounts, intelligent engineering frameworks embed generative AI, monitoring and automation into every stage of the product lifecycle from design to testing, deployment and continuous improvement, Tinaikar said.
• AI modernization tools allow developers to understand code that is no longer in use. “Even legacy code with no documentation can now be documented with AI tools,” he explained. 
• “By embedding continuous productivity improvement into modernization programs, (intelligent engineering) enables enterprises to update their digital surface and re-architect legacy systems into adaptive, future-ready platforms,” Tinaikar added.

But legacy modernization doesn’t come without security headaches. Risks include logic exposure from reverse-engineering, new configuration errors coupled with vulnerabilities, and supply chain compromises due to the reliance on third-party modernization tools, Nic Adams, co-founder and CEO of 0rcus, told CIO Upside. 

“This creates a dual-system (legacy and new) environment with increased attack surfaces, making it a high-risk period for exploitation,” said Adams. 

The temptation to rely on AI to handle legacy migration may be irresistible to executives. However, fully automated legacy modernization can backfire. 

“Introducing any automated code translation tools brings the risk of inheriting and replicating legacy vulnerabilities, security flaws and even insecure programming patterns in newly generated code,” Adams said.