Autonomous versus automated: What each means and why it matters
The terms autonomous and automated often get mixed up. When designing security strategies, knowing the distinctions between the two has its perks.
Many IT or security professionals evaluating cybersecurity solutions get tripped up over the definitions of “autonomous” and “automated.” Despite popular belief, these terms are not synonymous, but each carry a distinctive, separate meaning worth establishing when looking at security strategies.
I spoke with Scott Totman, vice president of engineering at DivvyCloud to discuss the differences between “autonomous” and “automated” solutions and to learn more about the best use cases of artificial intelligence/machine learning (AI/ML) in cybersecurity.
Scott Totman: The easiest way to distinguish between “autonomous” and “automated” is by the amount of adaptation, learning and decision making that is integrated into the system.
Automated systems typically run within a well-defined set of parameters and are very restricted in what tasks they can perform. The decisions made or actions taken by an automated system are based on predefined heuristics.
An autonomous system, on the other hand, learns and adapts to dynamic environments, and evolves as the environment around it changes. The data it learns and adapts to may be outside what was contemplated when the system was deployed. Such systems will ingest and learn from increasing data sets faster, and eventually more reliably, than what would be reasonable for a human.
It’s reasonable to view both automated and autonomous systems on a continuum. Systems that were originally automated with a well-defined set of inputs and outputs may need to become ‘smarter’ over time as their usage and the environment in which they operate change. Therefore, one could take an automated system and build in some autonomous capabilities, extending the useful life of the system and its overall applicability.
Looking at this another way, an automated system is one that’s instructed to perform a set of specific tasks with well understood parameters that are known ahead of time. It is built to perform a specific function repeatedly and in an efficient manner. An autonomous system is advising and helping to define what the right decision or action is under an evolving, non-deterministic environment.
Scott Totman: It completely depends on the problem being addressed. An autonomous system is often considered ‘superior’ simply due to the increased complexity in its processing capabilities.
However, if you build a system that is highly predictable and performs the same function repeatedly, then an automated system will provide superior value because it is simpler, easier to maintain and requires fewer resources to continue working. Leveraging autonomous systems for these types of solutions could result in the systems ‘learning’ incorrectly and therefore performing the wrong action. Autonomous systems will be truly superior in environments that can not exhaustively test for all conditions ahead of time and need to adapt/learn as the environment and other inputs evolve over time.
Scott Matteson: What are some real world examples of each?
Scott Totman: An example of an automated system is infrastructure and application level compliance checks within a corporation’s environment. These systems monitor against a well-defined set of compliance standards and inform the organization when systems fall out of compliance. These systems can also take well-defined actions to correct the issue, but this does not imply that they are autonomous.
They are explicitly configured to take a specific action, thereby allowing the organization to have confidence in exactly what is happening to their environments. More often than not, these systems simply flag an issue so that a user or administrator can go in and correct the issue.
