Machine learning can provide the fastest way to identify new cyber-attacks, draw statistical inferences, and push that information to endpoint security platforms.
Art Coviello, a partner at Rally Ventures and the former chairman of RSA summaries the value of automation for cybersecurity; “There are too many things happening – too much data, too many attackers, too much of an attack surface to defend – that without those automated capabilities that you get with artificial intelligence and machine learning, you don’t have a prayer of being able to defend yourself,” MIT Lincoln Labs Fellow Jeremy Kepner notes that “Detecting cyber threats can be greatly enhanced by having an accurate model of normal background network traffic,” and that analysts could compare the internet traffic data they are investigating with these models to bring anomalous behavior to the surface more readily.
Smart cybersecurity has a promising and large role to play in identifying, filtering, neutralizing and remediating cyber-threats.
By harnessing evolving enterprise tools such as artificial intelligence machine learning, automated and adaptive networks and supercomputing, enterprises will be more readily be able to meet the future challenges.
- Marketing Is Turning to AI for Customer Acquisition
- Business leaders discuss why it is important to pick the right technology for your business – rather than jump on the next hype
- How AI Accidentally Learned Ecology by Playing StarCraft
- 3 Ways Machine Learning Can Transform the Healthcare Industry
- 7 Digital Transformation Trends To Follow
- Four reasons why regional manufacturers must adopt AI
- How Digital Transformation Is Helping You Pay Your Mortgage
- Cisco Live 2021 – how COVID has accelerated digital transformation for three end-user organizations
- Championing Digital Transformation: Start With 8 Steps
- Digital transformation: you’re doing it all wrong