Measurement, Quantitative vs. Qualitative and Other Cool Stuff

Measurement,Qualitative vs. Quantitative Analysis,and other Cool Stuff
Presenting: Risk Centric Security, Inc.
www.riskcentricsecurity.com
Sponsor: Aliado
www.aliadocorp.com
Risk Centric Security, Inc. Confidential and Proprietary .
Copyright © 2011 Risk Centric Security, Inc . All rights reserved.
Risk Analysis for the 21st Century

Introductions

Heather Goodnight is an accomplished Global Sales and Business Development Consultant. Over the years, her unique, practical insight into problems of risk and opportunity have provided important guidance for organizations both large and small. She is a cofounder of Risk Centric Security and currently serves as President of the Corporation.
Patrick Florer has worked in information technology for 30 years. In addition, he worked a parallel track in medical outcomes research, analysis, and the creation of evidence-based guidelines for medical treatment. His roles have included IT operations, programming, and systems analysis. From 1986 until now, he has worked as an independent consultant, helping customers with strategic development, analytics, risk analysis, and decision analysis. He is a cofounder of Risk Centric Security and currently serves as Chief Technology Officer.
Risk Centric Security, Inc. Confidential and Proprietary .

Houston, we have a problem …
When speaking with our customers, we recognized:
Information Security Professionals are comfortable speaking the technical language of firewalls, logs, threats, vulnerabilities, and exploits.
Business managers are comfortable speaking the language of return on investment, discounted cash flows, and risk as financial impact.
Mutual misunderstanding can occur, and it is often a source of frustration for everyone.
Risk Centric Security, Inc. Confidential and Proprietary.

So how do we bridge the language gap?
By learning to speak about risk in business terms, Information Security Professionals can reach out and bridge the language gap.
The technical details of sql injection attacks may be important to you, but your business counterparts may not understand, and they usually don’t care.

So how do we bridge the language gap?
Instead of talking about threats, vulnerabilities, and controls, talk about risk in terms of financial impact. Tell the business people what a sql injection attack could cost.
They will understand that!
(They may not believe you, but they will understand what you are saying!)

What are we going to talk about today?
Risk
Risk and Opportunity
Possibility vs. probability
Measurement
Precision vs. accuracy
Qualitative vs. quantitative methods
The “not enough data” syndrome
Monte Carlo simulation
Modeling expert opinion and the PERT distribution

What is risk, anyway?
From The American Heritage dictionary*:
The possibility of suffering harm or loss; danger.
A factor, thing, element, or course involving uncertain danger; a hazard.
The danger or probability of loss to an insurer.
The amount that an insurance company stands to lose.
The variability of returns from an investment.
The chance of nonpayment of a debt.
*The American Heritage® Dictionary of the English Language, Fourth Edition. Copyright © 2006, 2000. Houghton Mifflin Company.

From ISO 31000:
1.1 risk - effect of uncertainty on objectives

NOTE 1 An effect is a deviation from the expected —positive and/or negative.
NOTE 2 Objectives can have different aspects (such as financial, health and safety, and environmental goals) and can apply at different levels (such as strategic, organization-wide, project, product and process).
NOTE 3 Risk is often characterized by reference to potential events (3.5.1.3) and consequences (3.6.1.3), or a combination of these.

NOTE 4 Risk is often expressed in terms of a combination of the consequences of an event (including changes in circumstances) and the associated likelihood (3.6.1.1) of occurrence.

NOTE 5 Uncertainty is the state, even partial, of deficiency of information related to, understanding or knowledge of, an event, its consequence, or likelihood

In the USA, NIST, Special Publication 800-30 describes risk in the following way:
Risk is:
“the net mission impact considering the probability that a particular threat-source will exercise (accidentally trigger or intentionally exploit) a particular information system vulnerability, and the resulting impact if this should occur.”

NIST (The National Institute of Standards and Technology), provides an additional definition of risk in Special Publication 800-39:
Risk

A measure of the extent to which an entity is threatened by a potential circumstance or event, and typically a function of: (i) the adverse impacts that would arise if the circumstance or event occurs; and (ii) the likelihood of occurrence.

Information system-related security risks are those risks that arise from the loss of confidentiality, integrity, or availability of information or information systems and reflect the potential adverse impacts to organizational operations (including mission, functions, image, or reputation), organizational assets, individuals, other organizations, and the Nation.
NIST, The National Institute of Standards and Technology, Special Publication 800-39, Appendix B, Page B-7.

What are the common elements here?
A probability that something will happen
A probable impact if something does happen

What, then, is our working definition of risk?
The probability that something will happen to cause a negative impact in financial terms:For example, a 50% chance that it will cost 50 million dollars if our data are stolen.
Another way to express this is to multiply the two numbers together and say that:
Risk = 25 million dollars on an annualized basis

Risk and Opportunity
For our discussion today, Risk will be used to indicate loss or harm.
Opportunity can be viewed as the positive aspect of Risk.
The techniques that apply to Risk analysis can also be applied to Opportunity analysis.

Possibility and Probability
Let’s look at tossing a coin:
What are the possibilities?
What are the probabilities?
Does knowing either help us predict what will happen when we toss the coin next time?

A possibility is something that is “capable of happening, existing, or being true without contradicting proven facts, laws, or circumstances known to be true. *”
A probability is "the likelihood that a given event will occur.”*
*All quotes from The American Heritage® Dictionary of the English Language, Fourth Edition. Copyright © 2006, 2000. Houghton Mifflin Company.

In statistics, a probability is “a number expressing the likelihood that a specific event will occur, expressed as the ratio of the number of actual occurrences to the number of possible occurrences.“
Probability is calculated after tossing the coin many times.
Probability is always a number between 0 and 1, sometimes expressed as:
0 <= P(X) <= 1

How can we use this in information security risk analysis?
The fact that something can happen (possibility) doesn't tell us how likely it is to happen (probability), or how much impact it might have if it does happen (probability).
Estimating these values helps us prioritize our activities in a rational way.

Measurement
What is a measurement?
An observation that “ascertains the dimensions, quantity, or capacity of” an object or process”*
A set of observations that reduce uncertainty where the result is expressed as a quantity**
*The American Heritage® Dictionary of the English Language, Fourth Edition. Copyright © 2006, 2000. Houghton Mifflin Company
** Hubbard, Douglas W., “How to Measure Anything 2nd Edition”, John Wiley & Sons, New Jersey, 2010, p. 23

Measurement
What are the properties of a measurement?
Validity – does the measurement actually do what you think it does?
Reproducibility – when repeated, does the measurement give a consistent answer?
Detail – does the measurement provide a useful level of detail?

Measurement
What are some sources of error in measurement?
Random error – a function of the instrument
Bias – a function of the measurement taker

Measurement
Why do we make measurements?
Measurements are a way to collect data.
Making measurements should be about reducing uncertainty.
A measurement only has to be good enough for the decision at hand.
Sometimes, you cannot get the data you think you need, so you have to use a proxy.

Precision and Accuracy
Precision is “the ability of a measurement to be consistently reproduced.”
Accuracy is “the ability of a measurement to match the actual value of the quantity being measured.”

Precision: a machine can produce the same part to within 1/1000th mm all day long. This is no guarantee that the part is the correct length, however.

Accuracy: a machine can produce the same part to within +/- 2/1000th mm of the correct length. Although some parts are a bit shorter and some are a bit longer, every part is within spec.


Precision: 100.001, or 10.233%

Accuracy: 100 or 10%, or 10.2%



Prefer Accuracy to Precision.
Precise Accuracy? – it would be nice!

Qualitative and Quantitative Methods
Qualitative methods: green, yellow, orange, and red (dashboards) or a scale from 0 – 5 (categorical, nominal, and ordinal).
Quantitative methods: real numbers (cardinal scale).
Most of the time, quantitative methods are easier.

Qualitative Methods
Benefits of qualitative methods?
They are useful in certain scenarios, and can be quick and good enough.
Problems with qualitative methods?
Variability between assessors
Inconsistency of a single assessor
Arithmetic and statistical operations not possible
Problems near the boundaries of categories
Loss of information

Qualitative Methods
Variability between assessors
Faced with the same set of facts, different assessors apply a scale differently.
Two QSA’s apply the PCI standards differently.
Two risk analysts classify risks differently – one says low, one say medium

Qualitative Methods
Inconsistency of a single assessor
Given the same set of facts, an assessor might make different assessments when the only difference is the passage of time.

Qualitative Methods
Difficulty with arithmetic and statistical operations
From ISO 17999

Qualitative Methods
Difficulty with arithmetic and statistical operations
Imagine if money worked this way:
The value of a dollar would be relative to the purchase price of an item.
The value of a dollar might vary from store to store.

Qualitative Methods
Problems with aggregation and estimates near the boundaries of categories
Assume that:
Low = < 1M
Medium = 1M – 5M
High = >5M

Qualitative Methods
And assume that the following risks have been identified and put into categories:
$100K, 500K, 800K: all in Low category
$1M, 3M, 3M, 4M: all in Medium category

Qualitative Methods
What happens when you aggregate based upon qualitative scales?
What is the real difference between a very “high Low” and a very “low Medium”?
How can we justify and defend category boundaries that are essentially arbitrary?

Qualitative Methods
Loss of information
Most of the time, we get a number in mind.
Then, we assign it to a category.
Why not just keep the number?
Or better yet, create a distribution around a range of estimates to better express our beliefs and confidence?

Quantitative Methods
Benefits of quantitative methods?
The numbers mean what they are (cardinality).
Arithmetic and statistical methods are possible.
Problems with quantitative methods?
Data are required.
Estimates are estimates – the future hasn’t happened yet.
Formal training in calibration techniques is very helpful.

”We don’t have enough data!”
They say: there isn’t enough “good” data, so you are just processing “garbage in and garbage out.”
The reason we need data is to reduce uncertainty in decision-making.
The decision we need to make will define the data we need – some decisions require very little data, others require quite a bit.

How much data do we really need?
A sample can be smaller than you think.
Parametric vs. non-parametric methods
Contact us for more information on these topics.

What is the definition of “good” data?
We often hear that the data are poor –
What does this mean?
Data are just data – some data may be more interesting than other data – it depends on what you are doing.

Interesting data collection projects
Dan Geer et al.:
The Index of Cybersecurity
(http://www.cybersecurityindex.org/)
Prediction Market Project
The Beewise Project
(http://beewise.org/markets/metricon.ctrl)

”We don’t have enough data!” - Sources
Please refer to the slides at the end of this presentation.

Monte Carlo Simulation
Monte Carlo simulation is a game changer for information security risk analysis.
Less sophisticated methods use single-point estimates or even simple ranges of estimates:
35%, or from 20% - 51%
Monte Carlo methods sample thousands or tens of thousands of values, and provide a much clearer picture of the possible outcomes.

The Beta Pert Calculator

Minimum:
What is the least or lowest (best or worst) numerical estimate that you believe to be reasonable? This will be the smallest number you come up with.

Most Likely:
What is the most likely or most probable numerical estimate in your opinion? This number must fall between the minimum and maximum. It may equal either the minimum or the maximum, but should not equal both

Maximum:
What is the greatest or highest (best or worst) numerical estimate that you believe to be reasonable?

Note that “best” or “worst” case estimates could be either minimum or maximum values, depending upon the scenario.
In a risk / loss exposure scenario, lower is better, so the minimum represents the lowest loss, or best outcome. The maximum represents the highest loss, or worst outcome.
In a sales or opportunity scenario, it’s the reverse: lower is not better, so the minimum represents the worst case. Higher is better, so the maximum represents the best case.

Confidence:
On a scale that includes “Very Low”, “Low”, “Average”, “High”, and “Very High”, how confident are you in the accuracy of your estimates?
This parameter controls the sampling around the most likely value, and thereby also controls the height of the histogram or slope of the cumulative plot.
For most analyses, using “Average” for the confidence parameter works well. In this instance, “Average” really means having no strong feeling about the matter – being evenly divided between under-confidence and over-confidence.

Percentile Tables

PercentileTables
1% of values are <= 10,044 and 99% are > 10,044
The 50th percentile has another name - it’s called the Median.

The Median is the mid-point in a list of values - half of the values in the list are less and half are greater than the Median.

Histogram

Cumulative Plot

Thank you !
Heather Goodnight
Patrick Florer
Co-founders
Risk Centric Security, Inc
heather@riskcentricsecurity.com
patrick@riskcentricsecurity.com
www.riskcentricsecurity.com
214.405.5789
Jody Keyser
Aliado
jkeyser@aliadocorp.com
Risk Analysis for the 21st Century

Open Security Foundation: datalossdb and osvdbhttp://www.opensecurityfoundation.org/
Computer Security Institute (CSI): http://gocsi.com/

Office of Inadequate Security: http://www.databreaches.net/

Identity Theft Resource Center: http://www.idtheftcenter.org/

ISACA: www.isaca.org

ISSA: www.issa.org

Mitre Corporation: www.mitre.org
OWASP: http://owasp.com/index.php/Main_Page
Privacy Rights Clearing House: http://www.privacyrights.org/

SANS: www.sans.org

The Ponemon Institute: www.ponemon.org

Conference procedings: Black Hat, RSA, Source Conferences, BSides

Internet tools:

Search engines: Google, Bing, Yahoo, Ask.com

Trend Analyzers:

Google trends: http://www.google.com/trends
Twitter Trends: www.trendistic.com

Amazon: http://www.metricjunkie.com/

Securitymetrics.org – mailing list
Society of Information Risk Analysts (SIRA)

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Measurement, Quantitative vs. Qualitative and Other Cool Stuff

by Jody Keyser on Jun 23, 2011

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Measurement, Quantitative vs. Qualitative and Other Cool Stuff — Presentation Transcript