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Security technologies are often described as either “automated” or “autonomous.” These words sound similar but have very different meanings. Both types of systems can provide immense value if used appropriately.
An automated drone system increases efficiency by eliminating the need for a drone operator, while providing seamless access to routine, frequent and real-time data. An autonomous drone, on the other hand, entails the absence of an operator that is responsible for the technology. Even at their most autonomous, however, drones still require an individual to preprogram their flight paths. A human sets the objective, the machine can then decide between a number of approaches based on other situational data it may have collected and the current status of the objective.
Darkreading.com explores the differences between them, and while it focuses on IT environment, the differences can relate to other fields as well.
Autonomous Solutions – An autonomous system learns and adapts to dynamic environments and makes decisions (or takes actions) based on ever-changing data. Such systems use machine learning (ML) and artificial intelligence (AI) to learn from data, and the more data they ingest, the better they learn. In certain applications, autonomous systems eventually will become more reliable than humans and will perform tasks at an efficiency level not humanly possible.
Automated Solutions run within a well-defined set of parameters that consistently execute the steps as defined. The decisions made or actions taken by an automated system are based on predefined rules, and the system will perform those decisions/actions perfectly every time, eliminating the possibility of human error.
The concern about autonomous systems stems from the fact that they might be deployed for the wrong purpose. For example, if you’re building a system that’s highly predictable and performs the same function repeatedly, then an automated system provides value because it is simpler, easier to maintain, and requires fewer resources to continue working. Leveraging autonomous systems for these types of solutions may wind up being overly complex relative to the job being performed and introduces unnecessary risks, such as the systems learning incorrectly and performing the wrong action in the future.
Autonomous solutions are best used when the full spectrum of possible scenarios is unknown, and therefore there are no predefined rules for how to respond to new situations. Self-driving cars are the go-to example of why autonomous solutions are necessary, because there are too many different variants for a rules-based approach.
In the world of cybersecurity, these solutions are important because hackers are constantly coming up with new attack methods. Suspicious activity that has never been seen before (and therefore no rules exist for it) could slip by an automated system, but this is what autonomous solutions are built to identify and respond to.
Use cases include the detection of anomalous activity in very large, complex data streams, the identification of unknown threats, etc.
Automated systems are best used in highly predictable scenarios and tasks for which a best practice already exists.