Hutton B Side Sf
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1) Current risk management approaches are problematic because they are either too notional and abstract or too focused on tangible metrics. 2) A new evidence-based approach is proposed that uses incident data frameworks to extract metrics that can be used to build models of threats, impacts, and management capabilities. 3) By analyzing patterns in incident data, more accurate assessments of risk can be made based on an organization's unique loss landscape, threat landscape, controls landscape, and how these change over time. This moves risk management from superstition to a measurable science.
Hutton B Side Sf
- 1. Risk Management Time to blow it up and start over? @alexhutton
- 2. Met E.T. Jaynes probability theory, the logic of science
- 3. Kuhn’s Protoscience A stage in the development of a science that is described by: • somewhat random fact gathering (mainly of readily accessible data) • a “morass” of interesting, trivial, irrelevant observations • A variety of theories (that are spawned from what he calls philosophical speculation) that provide little guidance to data gathering
- 4. only the wisest and stupidest of men never change Confucius
- 5. Destroy GRC Musings of a Risk Management Deconstructivist
- 6. A feeling of diss-connect between GRC and Security
- 8. governance, without metrics & models, is superstition governance, with metrics & models, describes capability to manage risk
- 9. Why does what you execute on and how you execute matter?
- 12. governance, without metrics & models, is superstition governance, with metrics & models, describes capability to manage risk measurably good governance practices (can/will) reduce risk measurably good governance is simply a description of capability to manage risk
- 13. not sucking eggs at security is a good idea
- 14. compliance*, without metrics, is superstition compliance*, with metrics, is risk management *(regulatory)
- 15. But “GRC” Risk Management Find issue, call issue bad, fix issue, hope you don’t find it again...
- 16. What is risk?
- 17. a. Risk is notional b. Risk is tangible
- 18. Problems with “tangible” - complex systems, complexity science - usefulness outside of the very specific - measurements - lots of belief statements
- 19. How Complex Systems Fail (Being a Short Treatise on the Nature of Failure; How Failure is Evaluated; How Failure is Attributed to Proximate Cause; and the Resulting New Understanding of Patient Safety) Richard I. Cook, MD Cognitive technologies Laboratory University of Chicago http://www.ctlab.org/documents/How %20Complex%20Systems %20Fail.pdf
- 20. Catastrophe requires multiple failures single point failures are not enough.. The array of defenses works. System operations are generally successful. Overt catastrophic failure occurs when small, apparently innocuous failures join to create opportunity for a systemic accident. Each of these small failures is necessary to cause catastrophe but only the combination is sufficient to permit failure. Put another way, there are many more failure opportunities than overt system accidents. Most initial failure trajectories are blocked by designed system safety components. Trajectories that reach the operational level are mostly blocked, usually by practitioners. Complex systems contain changing mixtures of failures latent within them. The complexity of these systems makes it impossible for them to run without multiple flaws being present. Because these are individually insufficient to cause failure they are regarded as minor factors during operations. Eradication of all latent failures is limited primarily by economic cost but also because it is difficult before the fact to see how such failures might contribute to an accident. The failures change constantly because of changing technology, work organization, and efforts to eradicate failures.
- 21. Complex systems run in degraded mode. Post-accident attribution accident to a ‘root cause’ is fundamentally wrong. All practitioner actions are gambles. Human expertise in complex systems is constantly changing Change introduces new forms of failure. Views of ‘cause’ limit the effectiveness of defenses against future events.
- 22. Problems with “notional” - becomes difficult to extract wisdom - we want a “Gross Domestic Product” - unable to be defended - pseudo-scientific - lots of belief statements
- 23. from Mark Curphey’s SecurityBullshit
- 24. What is risk?
- 25. uses of “risk” - engineering - complex systems says “no” - financial - no 110% return on your firewall - medical - requires data
- 26. our standards say: Find issue, call issue bad, fix issue, hope you don’t find it again...
- 27. Managing risk means aligning the capabilities of the organization, and the exposure of the organization with the tolerance of the data owners - Jack Jones
- 28. evidence based medicine, meet information security What is evidence-based risk management? a deconstructed, notional view of risk
- 29. Loss Landscape Threat Landscape risk Asset Landscape Controls Landscape
- 30. Loss Landscape a balanced scorecard? Asset Landscape Threat Landscape risk Controls Landscape
- 31. Loss Landscape a balanced scorecard? capability (destroys “g” introducing quality management & mgmt. Asset Landscape Threat Landscape science elements into infosec) risk exposure change “compliance” simply becomes a factor of loss landscape and/or operating as a Controls Landscape control group for comparative data
- 32. The Achilles heel again, lack of data
- 33. Models and data sharing Good Lord Of The Dance, something a vendor might actually help you with
- 34. Verizon Incident Sharing Framework it’s open*! * kinda
- 35. Verizon has shared data
- 36. - 2009 – over 600 cases - 2010 – between 1000 & 1400
- 37. Verizon is sharing our framework
- 38. What is the Verizon Incident Sharing (VerIS) Framework? - A means to create metrics from the incident narrative - how Verizon creates measurements for the DBIR - how *anyone* can create measurements from an incident - http://securityblog.verizonbusiness.com/wp-content/uploads/ 2010/03/VerIS_Framework_Beta_1.pdf
- 39. What makes up the VerIS framework? - Demographics - Incident Classification - Event Modeling (a4) - Discovery & Mitigation - Impact Classification - Impact Modeling
- 40. Cybertrust Security demographics - company industry - company size - geographic location - of business unit in incident - size of security department
- 41. Cybertrust Security incident classification - agent error misuse - what acts against us malware hacking environmental external - asset social action physical - what the agent acts against internal agent asset confidentiality possession - action partner availability - what the agent does to the type attribute utility asset function authenticity integrity - attribute - the result of the agent’s action against the asset
- 42. Cybertrust Security incident classification a4 event model the series of events (a4) creates an “attack model” 1 > 2 > 3 > 4 > 5
- 43. Cybertrust Security discovery & mitigation - incident timeline - discovery method evidence sources + - - control capability - corrective action - most straightforward manner in which the incident could be prevented - the cost of preventative controls
- 44. Cybertrust Security Impact classification - impact categorization - sources of Impact $ (direct, indirect) - similar to iso 27005/FAIR - impact estimation - distribution for amount of impact - impact qualification - relative impact rating
- 45. Cybertrust Security incident narrative incident metrics discovery demographics incident classification (a4) impact classification + & mitigation 1> 2> 3> 4 > 5 $$$
- 46. Cybertrust Security case studies data set discovery demographics incident classification (a4) impact classification + & mitigation a 1> 2> 3> 4 > 5 $$$ b 1> 2> 3> 4 > 5 + $$$ c 1> 2> 3> 4 > 5 + $$$ d 1> 2> 3> 4 > 5 + $$$ e 1> 2> 3> 4 > 5 + $$$ f 1> 2> 3> 4 > 5 + $$$
- 47. Cybertrust Security data set knowledge & wisdom discovery demographics incident classification (a4) impact classification + & mitigation a 1> 2> 3> 4 > 5 $$$ b 1> 2> 3> 4 > 5 + $$$ c 1> 2> 3> 4 > 5 + $$$ d 1> 2> 3> 4 > 5 + $$$ e 1> 2> 3> 4 > 5 + $$$ f 1> 2> 3> 4 > 5 + $$$
- 48. Cybertrust Security threat modeling discovery demographics incident classification (a4) impact classification + & mitigation a 1> 2> 3> 4 > 5 $$$ b 1> 2> 3> 4 > 5 + $$$ c 1> 2> 3> 4 > 5 + $$$ d 1> 2> 3> 4 > 5 + $$$ e 1> 2> 3> 4 > 5 + $$$ f 1> 2> 3> 4 > 5 + $$$
- 49. Cybertrust Security threat modeling discovery demographics incident classification (a4) impact classification + & mitigation a 1> 2> 3> 4 > 5 $$$ b 1> 2> 3> 4 > 5 + $$$ c 1> 2> 3> 4 > 5 + $$$ d 1> 2> 3> 4 > 5 + $$$ e 1> 2> 3> 4 > 5 + $$$ f 1> 2> 3> 4 > 5 + $$$
- 50. Cybertrust Security impact modeling discovery demographics incident classification (a4) impact classification + & mitigation a 1> 2> 3> 4 > 5 $$$ b 1> 2> 3> 4 > 5 + $$$ c 1> 2> 3> 4 > 5 + $$$ d 1> 2> 3> 4 > 5 + $$$ e 1> 2> 3> 4 > 5 + $$$ f 1> 2> 3> 4 > 5 + $$$
- 51. Cybertrust Security impact modeling discovery demographics incident classification (a4) impact classification + & mitigation a 1> 2> 3> 4 > 5 $$$ b 1> 2> 3> 4 > 5 + $$$ c 1> 2> 3> 4 > 5 + $$$ d 1> 2> 3> 4 > 5 + $$$ e 1> 2> 3> 4 > 5 + $$$ f 1> 2> 3> 4 > 5 + $$$
- 52. Problems: Data sharing, incidents, privacy Failures vs. Successes (where management capability helps) Talking to the business owner (might still need a “tangible approach here, but pseudo-actuarial data can help - we still want a GDP)
- 53. Successes: Bridge the gap (IRM becomes tactically actionable based on threat/attack modeling) (Capability measurements bridged to notional increase/decrease in risk) (complex system problems addressed by showing multiple sources of causes) Accurate, notional likelihood Accurate tangible impact
- 54. Requirements: Data Sets Models Technology Sciences - complexity, management/TQM/Probability/ Game Theory, biomimicry...