CIOs say that cybersecurity has been severely tested in the pandemic, and security professionals have turned to AI for better security measures
IT leaders point out that while the volume of data that needs to be secured has gone up significantly, threat levels have skyrocketed, data transfer and computing power speeds have increased as well.
They say that all these different networks- and online technologies are facing cyber risks at an even faster pace. Most organizations tend to overlook these factors, and this oversight ends up being a threat for them in the long-run.
Info security budgets are not relevant to the pace of change
CIOs say that most organizations tend to understaff their security department. Until recently, Artificial Intelligence for security was only a marketing gimmick. The maturity levels of AI being deployed were much lower than what they are now.
Today, it has evolved substantially and even new tools like Multi-layer artificial neural network functionality have been launched quite recently.
Since conventional software does not use AI, it is not capable of taking self-decisions. It is running on a list of pre-set conditions and instructions.
Security leaders point out that before the development of artificial neural networks, only a few AI-based programs were used for the basic approach using machine learning. CIOs say that while machine learning is smart, it requires a significant amount of manual input.
For majority sessions, humans are required to guide software on which features need analysis and observation by the program. This includes file type, extension, data transfer methods, etc.
The disadvantage of AI & ML
CIOs say that the major liability of using AI is that it’s been trained by humans with a certain mindset. As a result, fooling the ML model will be easy. Humans have to simply add an unprogrammed/ unexpected feature that will throw the program off track.
Artificial neural networks can however train AI to determine what functionalities are required for decision making. CIOs say that neural networks also require some degree of human input to double-check the conclusions. The platform will however self-organize the review and management of data to which it has access.
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How this helps security management
CIOs point out that continuous investment and research in the Artificial neural networks has bought about tech that was previously unchartered. These functionalities include data loss prevention, rogue identity blocking, detection and quarantining of malware, the configuration of security policies and devices, etc.
As the tech becomes more usable and stable, the price point has reduced over the years. The human component is quite minor and has moved the focus away from extensive configurations and manual testing.