- Understand the difference between threat detection and response (EDR) and threat prevention
- Understand the difference between machine learning and deep learning AI
When it comes to the impact of artificial intelligence (AI) on security, we first have to consider how security solutions provide security.
There are two general areas of focus within security – threat detection and response (EDR) and threat prevention. EDR is the traditional security solution that involves reactive support against threats. Prevention centric solutions focus on proactively identifying and responding to potential threats. An ideal security solution actually combines proactive prevention – to minimize risk – with reactive response – to minimize impact – but with a fair weighting towards prevention efforts.
Traditional security solutions have not involved prevention because it is has historically been challenging and expensive – imagine trying to anticipate extremely specific events. However, with the power of AI, prevention is becoming a democratized solution.
There are two types of AI: (1) machine learning and (2) deep learning. At a simplified high level, machine learning relies on a human element to identify what the AI should learn, while deep learning AI is completely self-taught. Both are incredibly valuable but which is better?
Deep learning is renowned by the industry as the most advanced subset of artificial intelligence. Why? With business security, code is always changing – very quickly. Humans can’t keep up with these changes to command machine learning but the deep learning method of analyzing 100% of raw data enables it to be highly effective. Among all other solutions to combat cyber threats, deep learning is proven to be most effective, resulting in unmatched detection rates and near zero false positives.
Marketing manager, Able One