Skip navigation

Better management through better measurement
Speakers: Wade Baker and Alex Hutton and Chris Porter

State of the industry: are we a science or pseudoscience?

  • random fact gathering
  • morass of interesting, trivial, irrelevant obs
  • variety of theories that provide little guidance to data gathering


Sources of knowledge under “risk” aggregate:

  • asset landscape
  • impact landscape
  • threat landscape
  • controls landscape


Risk Management:

Need to move from evidence-based practices (state of nature) to state of knowledge (lists, simple derived models w/ad-hoc monitoring, formal modeling) to wisdom (accomplishment, outcomes, constructs for decision making)


[side-talk: different perspectives on risk at different levels of the company]

[side-talk: science as data vs science as method…shld we have a systematic method? do methods just help acquire state of nature]


VERIS Framework

VZ A4 threat model

  • Agent: whose actions affected the asset
  • Action: what actions affected the asset
  • Asset: which assets were affected
  • Attribute: how asset was affected

set of metrics designed to describe security incidents; designed to provide a common language for describing security incidents (or threats) ina structured/repeatable manner; overall goal: foundation for risk mgmt…data driven decisions!

reduce risk; reduce spending


VERIS Community

1921 submissions to veris community since November. majority from probes and attacks. ~60 genuine incident submissions

[side-talk: why is VZ a player? mainly due to cybertrust acquisition; interesting discussion of why/how VZ views security as so important/strategic; product of converging IT & security practices]


VERIS Detailed Analysis

Chris explained some of the intricacies and digging a bit deeper. really need the slides. /me: this is why u shld have been at Metricon and not at yet-another cloud preso

“why group servers with apps instead of network devices?” – natural grouping since apps run on servers; often folks use “app” when it was really “server” – i.e. “my app got attacked” is more likely your “server got hacked”.

[side-talk: scenarios impacting assets; discussion about nuances between avail & util]

Can use this detailed analysis to map back to controls that would be relevant to this scenario (and potentially which ones failed or were missing completely)

Enables mapping of action types to identified vulnerabilities which can help prioritize actions to mitigate

[side-talk: how VZ constructs event chains for each attack]


A vision of EBRM Metrics

@alexhutton – baseball metrics view for exec dashboard. sample: frequency of incidents; peer comparison & gauge of impact :: can learn much from Jack Jones’ threat descriptions (/me: and I would argue the impact $ banding)

at the very least this will give us the ability to mature how we estimate loss value;

awesome point how this is really not like baseball: we don’t have comprehensive data like batter stats.


  1. “we don’t have comprehensive data like batter stats.”

    Not yet….

    But VERIS represents the *categories* of stats.

  2. er _one_ set of categories of stats. could be others, I just don’t know of any at VERIS’ level.

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.