DEBUG: PAGE=domain, TITLE=NelsonHall Blog,ID=1469,TEMPLATE=blog
toggle expanded view
  • NelsonHall Blog

    We publish lots of information and analyst insights on our blogs. Here you can find the aggregated posts across all NelsonHall program blogs and much more.

  • Events & Webinars

    Keep up to date regarding some of the many upcoming events that NelsonHall participates in and also runs.

    Take the opportunity to join/attend in order to meet and discover live what makes NelsonHall a leading analyst firm in the industry.


Subscribe to blogs & alerts:

manage email alerts using the form below, in order to be notified via email whenever we publish new content:

Search research content:

Access our analyst expertise:

Only NelsonHall clients who are logged in have access to our analysts and advisors for their expert advice and opinion.

To find out more about how NelsonHall's analysts and sourcing advisors can assist you with your strategy and engagements, please contact our sales department here.

Atos’ Use of Machine Learning for the Prescriptive SOC


When NelsonHall spoke to Atos earlier in the year about its managed security services, there was a clear push to move clients away from reactive security to a predictive and prescriptive security environment, so not only monitoring the end-to-end security of a client but also performing analytics on how the business and its customers would be affected by threats. Atos’ “Security at Heart” event two weeks ago provided more information on this.

I recently blogged about IBM’s progress in applying Watson to cybersecurity; Watson ingests additional sources such as security blogs into its data lake and machine learning to speed up threat detection and investigation. At face value, the prescriptive SOC offering from Atos isn’t very different in that it starts with a similar goal: use a wider set of security data sources and apply machine learning to better support clients.

With Atos’ prescriptive security approach, it has increased the amount of security data in the data lake that it analyzes. This information can come from threat intelligence feeds, contextual identity information, audit trails, full packet and DNS capture, social media, and information from the deep and dark web.

Atos highlights its ability to leverage its analytics and big data capabilities of its bullion high-end x86 servers to apply prescriptive analytics to the data in its data lake, then use the information, through McAfee’s DXL data exchange layer and threat defense life cycle, to automate responses to security events.

Using this capability Atos can reduce the number of manual actions that analysts are required to perform from 19 to 3. The benefits are clear; cyber analysts have more time to focus on applying their knowledge to secure the client and the speed, and completeness of the service offered increases. Atos claims its Prescriptive SOC analyzes 210 indicators of compromise compared to 6 in the previous service, reducing the time to respond to a threat from 24 hours to under seven minutes, and time to protect against a threat from 4.2 hours to around one minute.

Atos has been beta’ing its prescriptive managed security offering with several clients, mainly in the financial services sector.

Another highlight of the event was Atos’ Quantum computing capabilities, with the release of its Quantum Learning Machine (QLM) quantum computing emulator. These investments in quantum computing in effect future proof some of its cybersecurity capabilities.

The general consensus currently is that scale use of quantum computing by enterprises is still around a decade away. When this happens, quantum computing will add a powerful weapon to the threat actors’ arsenal: the ability to break current encryption methods. Atos' current investment in quantum computing, and specifically its quantum computing emulator, will help organizations develop and test today the quantum applications and algorithms of tomorrow. 

No comments yet.

Post a comment to this article: