Author: Infobelt

Data preservation safeguards valuable information to ensure its longevity and accessibility over time. As the volume and complexity of data continue to increase, traditional data preservation methods need to be revised. However, AI is changing the game by offering intelligent solutions enabling businesses to manage their data preservation needs proactively. Machine Learning (ML) algorithms can analyze patterns in data usage, predict potential issues, and optimize storage, ensuring critical information is safeguarded against loss. Additionally, AI-driven data preservation systems can automatically detect and repair corrupted files, reducing the risk of data degradation over time. By leveraging AI, businesses can achieve cost-effective, scalable, and reliable data preservation, supporting the seamless functioning of organizations across industries.

Safeguarding valuable data is pivotal for businesses, and archiving plays a crucial role. By systematically archiving data, companies can ensure the long-term preservation of critical information, even as technology evolves. This protection is vital for compliance, litigation, and historical records. Archiving prevents data loss due to accidental deletions, hardware failures, or cyberattacks, providing businesses with peace of mind and protection against potential disruptions.

Given the rapidly changing regulatory landscape, it can be difficult to understand regulatory compliance requirements to accurately assess and manage the associated risk. Let’s take a step back and start with the basics and explore how solutions like data archiving can help.

For example, redundant applications are frequently a result of:

The reasoning is simple: Larger, well-known firms are the ones most likely to be targeted by both cyber criminals and government agencies. Smaller firms—even moderately sized ones—will tend to “fly under the radar” of both, and so can put off investing in technology (RegTech) or training until the time when they have grown big enough to attract attention. In short, worrying about compliance or cybersecurity is a matter of scale, but only in the roughest sense: There are two size classes, bigger firms that need to worry about these things, and everyone else.

This group is sophisticated. It is patient. But the truly frightening part of the story was that the group was not using genius-level hacking techniques on modern technology. On the contrary: Their MO is to exploit known flaws in legacy Java applications running on Linux-based machines and web servers. Doing this carefully, the group has been able to create fraudulent transactions, thereby stealing small amounts of money over long periods. Taken altogether, the group managed to siphon millions unnoticed.

Which prompts the question: What is the difference? Why is backup not suitable for compliance, and what is gained from investing in a true enterprise data archive?

Financial services (“Finserv”) firms create more than their fair share of that data. Even a modest-sized regional bank will likely traffic in as much data as a company ten times its size. But what few experts have come to grips with is the sheer variety of data that finserv companies must manage.

But ransomware is, at its core, a scare tactic. As James Scott, Senior Fellow at the Institute for Critical Infrastructure Technology, puts it: “Ransomware is more about manipulating vulnerabilities in human psychology than the adversary’s technological sophistication.”