Solution saturation is prevalent in many organisations right now . Where historically they may have opted to adopt new tools in order to deliver quick fixes and plug gaps , this can culminate in security postures built on tens of different solutions from tens of different vendors .
Not only is this complex , requiring security staff to understand and manage multiple different accounts and dashboards , but it also makes it difficult to understand where overlaps and potential gaps lie .
AI and Machine Learning can unravel this complex web of tools , showing which solutions offer sustained value and which ones are rarely used , if ever , helping firms to minimise their operational overheads .
Automation begins with comprehensive , reliable data
The benefits of intelligent security tools are night and day with those driven almost entirely by human cognition . Yet achieving the former setup is easier said than done .
Indeed , these technologies will not begin to deliver immense benefits simply overnight . Rather , much like a new employee , they require time and knowledge of a specific network and environment before they can begin to support security teams effectively and accurately .
This knowledge has to come from reliable , comprehensive data – something that organisations may not be able feed into Machine Learning models right away . Without this , they will fail to develop the adequate intelligence
To deal with sophisticated threats , security responses need to be dynamic , requiring cuttingedge technologies .
Machine Learning and AI can become game-changing weapons in the fight against cybercrime .
needed to power accurate and informed detection and response activities .
For this reason , it is imperative that companies expand and organise their datasets , creating something of a data lake for security purposes in the first instance .
These data lakes should be continually evolving . In order for Machine Learning models to learn and operate effectively , they need to always map users , showing what they are doing , the applications they are using , how they are using them and at what times . This is vital to spotting anomalous activities which in turn can trigger an effective automated response .
To deal with sophisticated threats , security responses need to be dynamic , requiring cutting-edge technologies . Simply put , they can offer complete visibility of an organisation ’ s network , provide an appropriate response for any given threat , as well as unlock a stream of benefits relating to cost , efficiencies and operations .
Yet for these technologies to work , a dataled security mindset is non-negotiable .
Indeed , with time , patience and effective inputs in the form of clean , reliable , accurate and comprehensive datasets , Machine Learning and AI can become game-changing weapons in the fight against cybercrime . u www . intelligentciso . com