A surprising number of organizations rely on legacy systems, which face a number of challenges due to their archaic frameworks. Fortunately, many of these obstacles can be resolved with new developments in machine learning.
Security and Performance Risks Plague Legacy Systems
Security and performance problems are prevalent with legacy systems. Unfortunately, rectifying these problems can be costly. The United States government allocated $36 billion to legacy system administration and maintenance in 2011. Most of those costs could have been mitigated through modernisation.
Bobby Ford, global chief information security officer (CISO) at Unilever states that legacy systems are highly vulnerable to security breaches.
According to software developer and machine learning expert, Satyabrata Pal, a stunning 95% of all automated teller machines still use computer code written with COBOL. Many legacy systems depend on older programming languages, which have a lot of vulnerabilities. The Internal Revenue Service, one of the largest government institutions in the world, still uses a legacy system that was built in 1959. The IRS handles over a billion records, which are at risk of data loss or cyber-attacks.
Legacy systems such as automated teller machines and the IRS database have a tremendous impact on our daily lives. However, these issues are rarely considered unless a crisis erupts or a new report raises concerns. Many organizations are utilizing the most reliable legacy system modernization approaches. Machine learning applications plays a pivotal role in this process.
Administrators may want to focus on mainframe modernization, but machine learning could be useful for addressing concerns in the short-term.
Some of the potential benefits of improving legacy systems with deep learning technology are listed below.
There are a number of security risks inherent with legacy systems. Systems running older software are extremely vulnerable to hackers. Vendors have reduced the risk of cyber-attacks by providing ongoing support.
Machine learning can resolve these risks by helping upgrade legacy systems. This can facilitate digital transformation and considerably minimize risk factors.
Administrators can use deep learning technology to identify the biggest security concerns facing various legacy systems. Administrators can identify specific weak points in the system security architecture, which can be patched. Software development companies can develop programs to improve their legacy system.
Companies that provide security to legacy systems are using machine learning instead. Rather than depending on signatures to detect cyberattacks, they are looking at the behavior of hackers to bolster their defenses.
More Rapid Performance Testing
Legacy systems almost never live up to the same performance standards as more modern platforms. However, this does not mean that they can’t be improved upon with the programming languages that they have been developed with.
The problem is that many needed improvements never happen. Legacy system administrators often don’t even recognize shortcomings with the technology they are using. They might have become complacent with using the existing technology, without recognizing that the needs in their organization have changed. They may also have overlooked a degradation in the infrastructure itself, which could have caused the performance of the system to atrophy.
Machine learning technology can expedite performance testing. The organization can determine whether or not the system has failed to meet minimum expectations, so new improvements can be made as soon as possible.