The return of the prevention-first cyber security strategy

Cyber security is a top priority for most organisations, but as cyber criminals become more sophisticated and measured in their tactics, the number of complexities and cost of protecting your environment is increasing. Our Endpoint Security Business Manager, Dan Clipston explores how to overcome your cyber security challenges with a prevention-first approach.

Despite many great protective endpoint solutions that offer machine learning-based detection being available – a far cry from traditional signature-based anti-virus solutions – organisations are still being breached by zero day and sophisticated malware attacks.

One of the main reasons for this is that organisations often believe that they have adequate protection in place, but are unaware that the artificial intelligence behind the technology they are using isn’t advanced enough to prevent a breach. To fully protect your systems against cyber attacks, you need a prevention-first strategy and only a deep learning solution can provide this.

To understand this in more detail, we need to explore the differences between machine learning and deep learning.

Machine learning vs deep learning

Machine learning is a faster route to market for many security organisations and it requires less of an up-front investment to generate a financial outcome. Machine learning trains computers on a smaller set of data, which means it takes less time to train but gives less accuracy in the outcome.

Deep learning requires a much larger set of data and can take longer to train, but ultimately provides a much higher accuracy for the outcome. Deep learning is based on a very large and complex neural network that works like the human brain to make decisions. This enables the processing of unstructured data, such as documents, images, or text.

The differences between the two mean that a deep learning solution is much more accurate than a machine learning solution when preventing and defending against cyber attacks.

The benefits of a deep learning-based cyber security solution

Deep learning can look at 100% of the data it’s presented with, whereas machine learning can only see a subset of that data to make its decisions. It significantly reduces the number of false positives an organisation receives, while also improving your chances of capturing zero day and sophisticated malware attacks.

In addition, deep learning makes decisions much faster (milliseconds) than machine learning (seconds or minutes in some cases), and as every second counts when it comes to cyber attacks, the quicker you can respond, the better your defences are. Depending on how fast the processor is in the device that is being encrypted in a ransomware situation, taking seconds to respond can result in a significant loss of data.

With a deep learning-based endpoint solution, you can make predictions before a line of code even compiles on the hard drive, therefore giving a true prevention-first approach to cyber security.

Talk to us about protecting your organisation with a true prevention-first cyber security strategy.

As deep learning becomes more accessible to the wider market, we expect to see many more cyber security vendors adopt this approach. However, with only a limited number of deep learning-based cyber security solutions currently available, many organisations’ existing strategies need revising to guarantee full protection.

Chat to our cyber security specialist now who will support you to stay ahead of cyber criminals with a modern, prevention-first strategy, tailored for your organisation.

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Dan Clipston
Dan Clipston

Dan is Phoenix's cyber security endpoint specialist. He has four years of experience within the IT industry and works with our cyber security and GRCE vendors to deliver a core security message.

See all posts by Dan Clipston