Autonomous Data Protection

Will robots take over data management? In recent years, backup and disaster recovery system vendors have introduced several significant innovations. But the best is yet to come.

Modern data protection solutions, encompassing backup, disaster recovery, replication, and deduplication, are constantly evolving. Manufacturers have moved from a stage of manual configuration to automation. However, this is not the end of the road. There is increasing talk about the era of autonomous backup and even autonomous data management. Is this a near future reality, or just a fantasy?

Opinions on this matter are divided. Skeptics cite the example of autonomous cars. Although prototypes have appeared on the streets of San Francisco, the road to their widespread adoption seems to be a long way off. On the other hand, proponents point to robotic vacuum cleaners that are displacing traditional vacuum cleaners from homes. If humans can be eliminated from processes that require high precision, why not do the same in areas closely related to IT?

Automation and autonomy are very similar concepts, sometimes incorrectly used interchangeably. Nevertheless, there are some subtle differences between them. Automation means that the tasks performed are based on pre-defined parameters that must be updated as the situation changes. This is how elevators, office software, washing machines, robotic assembly lines, and most backup and DR systems work.

On the other hand, autonomous processes differ from automated ones in that they are constantly learning and adapting to the environment. In such cases, human intervention is not needed or is minimal. A great example is the aforementioned robotic vacuum cleaners or driverless cars.

The authors of the concept of autonomous data management assume that processes should take place invisibly, although under human control. Autonomy somehow combines automation with artificial intelligence (AI) and machine learning (ML), so that the data protection system intuitively adapts to the situation.

AI and ML technologies enable the automation of data management processes and minimize human intervention and supervision. Proponents of such a solution argue that it increases operational efficiency, extends uptime, improves security, and the level of services offered.

Clouds Force Change

If companies only stored data in on-premises environments, it would be possible to do without autonomous tools, but in the last two years, things have become much more complicated. Enterprises have moved some of their assets to the public cloud, which has contributed to the growing importance of hybrid and multi-cloud environments. It was supposed to be easier and cheaper, but the ongoing adoption of cloud services is causing sleepless nights for many IT managers.

The main problem lies in the excessive dispersion of data, which is located both in the local data center and in external service providers such as Amazon, Google, Microsoft, or smaller local providers. Managing, and especially protecting, digital assets scattered across various locations is a challenge. The situation is worsened by the relatively narrow range of vendors’ tools optimized for managing corporate data for hybrid and multi-cloud environments.

Part of the products provide support for multiple clouds through centralized control, although they consume many expensive resources. There are also efficient solutions, but only within a single cloud environment. Their main drawback is scalability in the clouds of different providers. In any case, in both of the aforementioned cases, operating costs are higher than desired.

Another problem is the excessive haste in implementing cloud technologies, leading to an increase in the number of point solutions. Cloud environment architects, application developers, and analysts implement independent data management solutions, which deepens the chaos and limits the possibilities of central management.

The data protection strategy in the cloud environment also leaves much to be desired. Security specialists emphasize that in today’s world, the most effective way to stop attackers is through preventive measures. Unfortunately, most modern technologies take a passive approach to resources stored in the cloud. In practice, this means that they create backups and restore backups after an attack, which results in unplanned downtime.

In summary, autonomous backup supports operations in multiple clouds, eliminates functional silos, automates all processes with minimal human intervention, and increases cyber resilience through active methods of detecting and preventing ransomware attacks.

To Limit the Role of the Weakest Link

It has long been known that people are the weakest link in the data protection system. This is particularly evident in environments that require fast and data-driven decision-making. It is also undeniable that people are prone to errors and slower than AI-based solutions, especially when it comes to mundane, repetitive tasks.

So will robots send IT department employees to the pasture in the near future? So far, no one is talking about it loudly. According to the authors of the concept of autonomous data management, the best solution in a complex, hybrid and multi-cloud environment is autonomous work. This means that data will self-optimize and repair itself, as well as move between different environments. Self-optimization uses artificial intelligence and machine learning to adapt to the principles and services related to data protection and management. Self-healing is the ability to predict, identify, and correct service errors or performance issues.

On the other hand, self-service assigns appropriate protection policies and manages and deploys applications and services without human intervention. What does this mean?

In the traditional model, a programmer deploying a new application relies on manual processes, which lengthens it. Autonomous data management eliminates all manual tasks, while protecting the application throughout the process, without the need for additional actions on the part of the application developer or IT staff.

Autonomous Data Management – Is It Worth It?

The concept of autonomous data management looks very promising. Importantly, some backup and DR system vendors are announcing the launch of such solutions in the near future, not in the coming years. On the market, you can already find products that use Machine Learning to early detect anomalies that signal an attempt to attack the backup system. Some companies also use partially AI-based solutions combined with DLP systems, which helps classify and tag information, and thus copy and protect the most important data.

However, only the widespread adoption of systems that provide autonomous data management will allow us to answer the fundamental question – is it worth the effort?

Some data protection specialists warn against excessive optimism. In their opinion, the biggest obstacle to the adaptation of autonomy in backup and DR processes may be the collection of a sufficiently wide range of data to be able to analyze various scenarios. It is difficult to imagine that vendors of solutions would share such information with each other.

It is also difficult to count on the openness of IT department employees, as they may fear that new products will deprive them of their jobs. It can also be safely assumed that the term “autonomy” will be overused by marketers, which on the one hand encourages customer investment, and on the other hand, threatens that low ratings of disappointed users will deter potential customers. It is possible that there will be limitations related to computing power, as well as the costs of such a solution. Nevertheless, it is worth closely following such initiatives, especially as it concerns large companies and institutions storing data in different environments.

Storware develops towards autonomous

While full autonomy might still be a distant goal, Storware’s focus on AI and automation is a significant step in that direction. These features have the potential to significantly improve efficiency, reduce human error, and enhance overall data protection.

In the near future, Storware will implement a number of improvements that will allow for:

• Automation: The Backup Assistant and conversational layer aim to automate routine tasks and provide intelligent responses, reducing human intervention.
• Intelligence: Storebrain’s ability to learn from collective data and provide optimal configurations demonstrates a move towards intelligent decision-making.
• Proactive Protection: The integration of AI into Isolayer for threat prevention showcases a proactive approach to data management, essential for autonomous systems.

However, key to achieving full autonomy would be further development in areas like:

• Self-healing capabilities: The system should be able to identify and resolve issues independently.
• Predictive analytics: Accurate forecasting of system behavior and potential problems.
• Continuous learning: The system should constantly improve its performance based on new data and insights.

Learn more [HERE]. Test Storware Backup and Recovery and share your path to responsible business data security with us.