For a few years, major R&D groups in tier-1 banking, academia, defence & intelligence (to name but a few), have cottoned on that the vast data sources in organisations and in the public domain, could help predict cyber security breaches and attacks and in so doing, help minimise their impact.
With the cyber security skills shortage we hear so much about, the case for AI-approaches to a big data opportunity was a no-brainer (incidently, the next wave of innovation will use small data & code analysis, and in a more medium-long term: computational language will turn on its head!).
With everything now becoming an endpoint (we humans (biotech), mobile, autonomous cars/ transport etc), expect to see more cyber security innovations (more preventative than detective cybersec) at the edge and silicon level over time.
Supervised and unsupervised machine learning approaches (and the advances in deep learning methods and algorithms) has seen analytical false positives reduce (ie reduced noise for the analyst to filter) and viable solutions hit the market. Its no surprise that VCs and Tech/ ISPs are adding these products to their portfolios – the global cost of cybercrime has now reached as much as USD$600 bn — about 0.8% of global GDP.
This is the tip of the iceberg of many innovative approaches to the problem to come. There is some interesting R&D currently in AUS, Germany & US, as a start. Will post soon.
some interesting investments to watch:
NAB Ventures investment in Digital Shadows
image credit: Gordon Johnson, pixabay
By Daniella Traino- Cyber Security Editor IdeaSpies