 |
VOOZH | about |
|
VOOZH | about |
We’re so glad you’re here. You can expect all the best TNS content to arrive Monday through Friday to keep you on top of the news and at the top of your game.
Check your inbox for a confirmation email where you can adjust your preferences and even join additional groups.
Follow TNS on your favorite social media networks.
Become a TNS follower on LinkedIn.
Check out the latest featured and trending stories while you wait for your first TNS newsletter.
Despite rapid technological improvements, many large enterprise organizations still rely on decades-old legacy IT systems. These systems can increase security vulnerabilities and bugs and prevent development teams from delivering software on schedule.
However, IT modernization can be a time- and resource-intensive process. While many organizations understand the long-term payoff of modernization, it can be challenging to justify the short-term investment, as it may be years before they see the returns.
Memory-unsafe languages are deeply integrated into large systems, and organizations have witnessed the resulting vulnerabilities. In fact, 70% of vulnerabilities may be due to legacy systems and memory-unsafe languages. Older code can be difficult to decode and update to a modern memory-safe language or application structure. Developers also require experience and a deep understanding of multiple languages to translate them to modern coding languages.
Incorporating proactive AI agents can support developer teams in their modernization efforts, allowing human teams to focus on strategizing and creating innovative products for customers. Organizations can introduce AI in their modernization efforts in three key ways: explaining older programming languages, assisting in greenfield code development and expediting vulnerability remediation.
Traditional refactoring techniques include inline refactoring, which restructures outdated elements of code, and abstraction, which removes duplicate code. But these conventional approaches are time-consuming, require skilled developers familiar with outdated languages and demand extensive testing to ensure their effectiveness.
A team of junior developers is unlikely to have the necessary level of insight and context into the legacy languages used in existing codebases. This makes deciphering the source code a daunting and time-consuming task. AI agents can translate the existing code into natural language and then generate new code in a memory-safe language for developers to review and test.
The new code can then be run on cloud infrastructure, using microservices or other available compute resources. As a result, the new code can perform the same function more efficiently and securely, with additional scalability and faster runtime responses.
In addition to updating legacy code, AI can be used to develop greenfield applications based on use cases or functions. Developers can use natural language for the required inputs, and AI can develop frameworks and code blocks to support those functions, even writing significant portions of applications using modern architectures.
AI can also enable collaboration between human collaborators. AI tools can summarize comments in code discussions, flag potential integration issues and track compliance requirements, streamlining communication between distributed development teams.
Security has become even more of a shared responsibility between security professionals and developers. The near-constant threat of bad actors has increased significantly with the rise of AI-powered threats, which target known vulnerabilities in legacy applications and outdated infrastructure. Security teams must leverage AI to match the speed of these attacks.
AI can be used to analyze existing code for patterns in user behavior, perform root cause analysis, automate security testing and implement fixes for vulnerabilities. AI enables better collaboration between security teams and developers, equipping developers to identify and manage vulnerabilities independently, which in turn lightens the workload of security teams.
This collaboration between AI, developers and security teams shows great promise for driving rapid responses to potential threats and reducing the response time to new threats from days to hours.
While the transition from legacy codebase maintenance to modernization may seem daunting, it is a crucial step toward ensuring organizational security and future-proofing operations. Rather than allocate valuable resources to managing and analyzing outdated languages and frameworks, development teams can focus on delivering value through innovative product development. Modernized code can help streamline processes, improve efficiency and drive innovation, all while reducing costs.
By modernizing our legacy codebases, we can eliminate an entire class of vulnerabilities and improve security across the broader tech ecosystem. AI will serve as both an accelerator and a guardian within this future.
