Introduction to scenario based risk analysis part 3

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This document discusses utilizing the results of scenario based risk analysis. It reviews parts 1 and 2, discusses simulations and analysis output. It then provides a case study on analyzing the risk of a lost or stolen laptop containing personal health information. The key steps of defining the scenario, elements, gathering data, documenting assumptions, running a Monte Carlo simulation, and interpreting the results are covered. Finally, it discusses analyzing the results in terms of financial, privacy and patient safety risk, prioritizing risk issues, and information for contacting HealthGuard.

Technology

© 2017 HealthGuard
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Introduction to
Scenario Based Risk Analysis
Part 3: Utilizing The Results

© 2017 HealthGuard
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Our Speakers
John Zuziak, Director of Information
Security, Catholic Health Initiatives
Apolonio “Apps” Garcia,
Founder/President, HealthGuard

© 2017 HealthGuard
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© 2017 HealthGuard
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Review (parts 1 & 2)
Simulations
Analysis output

© 2017 HealthGuard
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© 2017 HealthGuard
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Review:
Parts 1 & 2

© 2017 HealthGuard
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© 2017 HealthGuard
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Why do Scenario Based Risk Analysis?
“Scenarios are a powerful tool in a risk manager’s
armory—they help professionals ask the right questions and
prepare for the unexpected. Scenario analysis has become
a ‘new’ and best practice in enterprise risk management
(ERM). - ISACA

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© 2017 HealthGuard
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Threat Asset Loss/Harm
Event
Controls
Key Parts of a Scenario

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© 2017 HealthGuard
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Simulations

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© 2017 HealthGuard
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Monte Carlo Simulation

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© 2017 HealthGuard
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Case Study: Chief Privacy
Officer asks you about laptop
encryption

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© 2017 HealthGuard
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What is the risk associated with a lost/stolen
laptop that contains PHI?
Step 1: Define the scenario we want to analyze

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© 2017 HealthGuard
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Threat Asset Loss/Harm
Event
Controls
Step 2: Define the 3 key elements

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© 2017 HealthGuard
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Step 3: Gather data

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Step 4: Document Assumptions

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Step 5: Enter Values & Run Simulation

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© 2017 HealthGuard
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Interpreting & Utilizing
the Output

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© 2017 HealthGuard
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Financial Risk Privacy RiskPatient Safety
Analyzing the Results

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© 2017 HealthGuard
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Prioritizing Risk Issues

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© 2017 HealthGuard
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How to contact or follow us!
@healthguardsec
HealthGuard Security
www.healthguardsecurity.com

Threat hunting has been primarily a playground for security experts in surfacing unknown threats. It is a proactive security approach where the hunt starts with a hypothesis about a hidden threat that may be already in the enterprise network. According to 2017 survey on threat hunting by the SANS Institute, nearly 45% of organizations hunt on an ad hoc basis. The ad hoc approach is ineffective and does not yield sufficient results to cover the cost of threat hunting. Considering the scarcity of security analysts, the ad hoc threat hunting becomes a costly and expensive process. Also, threat hunting is typically performed by doing outlier detection of the data. For example, analysts usually do outlier detection to find suspicious processes out of Windows process logs. The outlier detection can be done using simple box plots, control charts, or using more sophisticated unsupervised machine learning techniques. However, the output of all the outlier detection techniques is outliers/anomalies that still need to be audited/investigated by the security analysts. This adds more workload to the already overwhelmed security analyst. The fusion of data science and deceive security provides an opportunity to validate many alerts automatically and therefore provides an automated approach from threat hunting. Deceptive defense system offers a way to confirm an adversary presence with nearly 0% false alarms when the adversary bumps onto one of the deceptions. The modern set of deceptions is the reincarnation of honeypots, honeytokens, honeynets, and honey files that blends well within the network and can dynamically change their configurations. When an adversary access a deception, it raises a positive affirmation of a threat. In this approach, one needs to use alerts and contextual security events along with deceptive security to rank the existing alerts. It takes away a lot of manual verification of various security alerts.

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