Hi [[ session.user.profile.firstName ]]

Machine Learning and Microsegmentation

“Make the trust zones smaller” is the rallying cry behind microsegmentation projects. Break up the network into smaller pieces and put firewalls in between those pieces to ensure attackers can’t get from one part of the network to the next. But making microsegmentation projects work relies on a great deal of knowledge of the network and attack pathways. And it’s an incredibly heavy lift–configuring and deploying a microsegmentation solution takes an immense amount of time and resources. Machine learning can significantly reduce the hurdles by learning the network’s intended state and defining the attack pathways that need to be secured, automating the microsegmentation process.

Edgewise’s Chief Data Scientist John O’Neil will deep-dive into the role of machine learning in microsegmentation and demonstrate how a machine learning driven solution could reduce the time and energy needed to deploy microsegmentation from months to hours.
Recorded Jan 18 2018 37 mins
Your place is confirmed,
we'll send you email reminders
Presented by
John O'Neil, Edgewise Chief Data Scientist
Presentation preview: Machine Learning and Microsegmentation

Network with like-minded attendees

  • [[ session.user.profile.displayName ]]
    Add a photo
    • [[ session.user.profile.displayName ]]
    • [[ session.user.profile.jobTitle ]]
    • [[ session.user.profile.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(session.user.profile) ]]
  • [[ card.displayName ]]
    • [[ card.displayName ]]
    • [[ card.jobTitle ]]
    • [[ card.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(card) ]]
  • Channel
  • Channel profile
  • Machine Learning and Microsegmentation Recorded: Jan 18 2018 37 mins
    John O'Neil, Edgewise Chief Data Scientist
    “Make the trust zones smaller” is the rallying cry behind microsegmentation projects. Break up the network into smaller pieces and put firewalls in between those pieces to ensure attackers can’t get from one part of the network to the next. But making microsegmentation projects work relies on a great deal of knowledge of the network and attack pathways. And it’s an incredibly heavy lift–configuring and deploying a microsegmentation solution takes an immense amount of time and resources. Machine learning can significantly reduce the hurdles by learning the network’s intended state and defining the attack pathways that need to be secured, automating the microsegmentation process.

    Edgewise’s Chief Data Scientist John O’Neil will deep-dive into the role of machine learning in microsegmentation and demonstrate how a machine learning driven solution could reduce the time and energy needed to deploy microsegmentation from months to hours.
  • Never Trust, Always Verify Recorded: Dec 6 2017 46 mins
    Harry Sverdlove, Founder and CTO of Edgewise Networks
    The perimeter model of network security is broken – attackers have little difficulty in spoofing IP addresses or piggybacking malicious software on top of policy-permitted network traffic to gain access to the network. And once inside the network, it's a simple task for attackers to move laterally until they find the pot of gold and exfiltrate.

    Harry Sverdlove, Founder and CTO of Edgewise, took a look at the four steps to implementing a zero trust methodology in your network:

    - Identify your assets
    - Map your workloads
    - Implement intent-based security policies
    - Continuously monitor and adapt to workload changes
  • Zero Trust Networking 101 Recorded: Oct 25 2017 49 mins
    Doug Barth and Evan Gilman
    Join Evan Gilman and Doug Barth, authors of the new O'Reilly book Zero Trust Networks, and learn about zero-trust networking and how it's going to improve the way workloads are protected in the cloud and data center.

    Evan and Doug explained why network security has to change, how zero trust networking rethinks the entire approach to workload protection, and what an effective zero trust network looks like in practice.
  • Five Ways to Better Protect Application Workloads in the Cloud Recorded: Sep 28 2017 16 mins
    Peter Smith, Founder and CEO of Edgewise Networks
    Peter Smith, Founder and CEO of Edgewise Networks, explained five ways to more effectively protect cloud and data center workloads and stop attack progression in a recent webinar.

    During the webinar, Peter explained how to:

    - Effectively translate application speak to network speak
    - Dramatically simplify policy management and remove complexity
    - Easily prioritize applications that are in urgent need of protection
    - Clearly demonstrate risks and benefits with measurable metrics
    - Ensure developers and application owners can quickly release new features
Edgewise Protects Where Firewalls Fail
Edgewise Networks stops attack progression in the cloud and data center by allowing only trusted applications to communicate over approved network pathways.

Embed in website or blog

Successfully added emails: 0
Remove all
  • Title: Machine Learning and Microsegmentation
  • Live at: Jan 18 2018 7:00 pm
  • Presented by: John O'Neil, Edgewise Chief Data Scientist
  • From:
Your email has been sent.
or close