Over the last several years not a month has gone by where I have not heard someone mention R – with regards to risk analysis or risk modeling – either in discussion or on a mailing list. If you do not know what R is, take a few minutes to read about it at the project’s main site. Simply put, R is a free software environment for statistical computing and graphics. Most of my quantitative modeling and analysis has been strictly Excel-based, which to date has been more then sufficient for my needs. However, Excel is not the ‘end-all-be-all’ tool. Excel does not contain every statistical distribution that risk practitioners may need to work with, there is no native Monte Carlo engine and it does have graphing limitations short of purchasing third party add-ons (advanced charts, granular configuration of graphs, etc…).

Thanks to some industry peer prodding (Jay Jacobs of Verizon’s Risk Intelligence team and Alex Hutton suggesting that ‘Numbers’ is a superior tool for visualizations). I finally bit the bullet, downloaded and then installed R.  For those completely new to R you have to realize that R is a platform to build amazing things upon. It is very command-line like in nature. You type in instructions and it executes. I like this approach because you are forced to learn the R language and syntax. Thus, in the end you will probably understand your data and resulting analysis much better.

One of the first graphics I wanted to explore with R was heat maps. At first, as I was thinking a standard risk management heat map; a 5×5 matrix with issues plotted on the matrix relative to frequency and magnitude. However, when I started searching Google for ‘R heat map’, a similar yet different style of heat map – referred to as a cluster heat map – was first returned in the search results. A cluster heat map is useful for comparing data elements in a matrix against each other depending on how your data is laid out. It is very visual in nature and allows the reader to quickly zero in on data elements or visual information of importance. From an information risk management perspective, if we have quantitative risk information and some metadata, we can begin a discussion with management by leveraging a heat map visualization. If additional information is needed as to why there are dark areas, then we can have the discussion about the underlying quantitative data. Thus, I decided to build a cluster heat map in R.

I referenced three blogs to guide my efforts – they can be found here, here and here. What I am writing here is in no way a complete copy and paste of their content because I provide some additional details on some steps that generated errors for me that in some cases took hours to figure out. This is not unexpected given the difference in data sets.

Let’s do it.

1.    Download and install R. After installation, start an R session. The version of R used for this post is 2.14.0. You can check your version by typing version at the command prompt and pressing ENTER.

2.    You will need to download and install the ggplot2 package / library. Do this through the R gui by referencing an online CRAN repository (packages -> install packages …). This method seems to be cleaner then downloading a package to your hard disk and then telling R to install it. In addition, if you reference an online repository, it will also grab any dependent packages at the same time. You can learn more about ggplot2 here.

3.    Once you have installed the ggplot2 package, we have to load it into our current R workspace.

> library(ggplot2)

4.    Next, we are going to import data to work with in R. Download ‘risktical_csv1.csv’ to your hard disk and execute the following command. Change the file path to match the file path for where you saved the file to.

risk <- read.csv(“C:/temph/risktical_csv1.csv”, sep=”,”, check.names= FALSE)

a.    We are telling R to import a Comma Separated Value file and assign it to a variable called ‘risk’.
b.    Read.csv is the method or function type of import.
c.    Notice that the slashes in the file name are opposite of what they normally would be when working with other common Windows-based applications.
d.    ‘sep=”,”’ tells R what character is used to separate values within the data set.
e.    ‘check.names=FALSE’ tells R not to check the column headers for correctness. R expects to see only letters, if it sees numbers, it will prepend an X to the column headers – we don’t want that based off the data set we are using.
f.    Once you hit enter, you can type ‘risk’ and hit enter again. The data from the file will be displayed on the screen.

5.    Now we need to ‘shape’ the data. The ggplot graphing function we want to use cannot consume the data as it currently is, so we are going to reformat the data first. The ‘melt’ function helps us accomplish this.

risk.m <- melt(risk)

a.    We are telling R to use the melt function against the ‘risk’ variable. Then we are going to take the output from melt and create a new variable called risk.m.
b.    Melt rearranges the data elements. Type ‘help(melt)’ for more information.
c.    After you hit enter, you can type ‘risk.m’ and hit enter again. Notice the way the data is displayed compared to the data prior to ‘melting’ (variable ‘risk’).

6.    Next, we have to rescale our numerical values so we can know how to shade any given section of our heat map. The higher the numerical value within a series of data, the darker the color or shade that tile of the heat map should be. The ‘ddply’ function helps us accomplish the rescaling; type ‘help(ddply)’ for more information.

risk.m <- ddply(risk.m, .(variable), transform, rescale = rescale(value), reorder=FALSE)

a.    We are telling R to execute the ‘ddply’ function against the risk.m variable.
b.    We are also passing some arguments to ‘ddply’ telling it to transform and reshape the numerical values. The result of this command produces a new column of values between 0 and 1.
c.    Finally, we pass an argument to ‘ddply’ not to reorder any rows.
d.    After you hit enter, you can type ‘risk.m’ and hit enter again and observe changes to the data elements; there should be two new columns of data.

7.    We are now ready to plot our heat map.

(p <- ggplot(risk.m, aes(variable, BU.Name)) + geom_tile(aes(fill = rescale), colour = “grey20″) + scale_fill_gradient(low = “white”, high = “red”))

a.    This command will produce a very crude looking heat map plot.
b.    The plot itself is assigned to a variable called p
c.    ‘scale_fill_gradient’ is the argument that associates color shading to the numerical values we rescaled in step 6. The higher the rescaling value – the darker the shading.
d.    The ‘aes’ function of ggplot is related to aesthetics. You can type in ‘help(aes)’ to learn about the various ‘aes’ arguments.

8.    Before we tidy up the plot, let’s set a variable that we will use in formatting axis values in step 9.

base_size <- 9

9.    Now we are going to tidy up the plot. There is a lot going on here.

p + theme_grey(base_size = base_size) + labs(x = “”, y = “”) + scale_x_discrete(expand = c(0, 0)) + scale_y_discrete(expand = c(0, 0)) + opts(legend.position = “none”, axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size * 0.8, angle = -90, hjust = 0, colour = “black”), axis.text.y = theme_text(size = base_size * 0.8, angle = 0, hjust = 0, colour = “black”))

a.    ‘labs(x = “”, y = “”)’ removes the axis labels.
b.    ‘opts(legend.position = “none”’ gets rid of the scaling legend.
c.    ‘axis.text.x = theme_text(size = base_size * 0.8, angle = -90’ sets the X axis text size as well as orientation.
d.    The heat map should look like the image below.

A few final notes:

1.    The color shading is performed within series of data, vertically. Thus, in the heat map we have generated, the color for any given tile is relative to the tile above and below it –IN THE SAME COLUMN – or in our case for a given ISO 2700X policy section.

2.    If we transposed our original data set – risktical_cvs2 – and applied the same commands with the exception of replacing BU.Name with Policy in our initial ggplot command (step 7), you should get a heat map that looks like the one below.

3.    In this heat map, we can quickly determine key areas of exposure for all 36 of our fictional business units relative to ISO 2700X. For example, most of BU3’s exposure is related to Compliance, followed by Organizational Security Policy and Access Control. If the executive in that business unit wanted more granular information in terms of dollar value exposure, we could share that information with them.

So there you have it! A quick R tutorial on developing a cluster heat map for information risk management purposes. I look forward to learning more about R and leveraging it to analyze and visualize data in unique and thought-provoking ways. As always, feel free to leave comments!

Self-Improvement. At some point while serving in the Marine Corps it became very obvious that there were three performance paths: be a bad performer and let the system make your life a living heck, be an average performer and let the system carry you along, be a stellar performer and push the system to its limits and possibly change it. I have always chosen to chase after stellar and it has worked pretty well for me over the years. However, in some professions to maintain stellar status – you have to constantly be seeking self-improvement.

Feedback. The term feedback means different things depending on the context in how it is being used. I find the act of feedback to be challenging both on the giving end as well as the receiving end – especially when it is feedback that is not complimentary. I have had both great and absolutely horrendous experiences – as an actor in both roles. The reality is that having feedback mechanisms in place whether formal or informal is critical to have – regardless of the merit of the feedback or how the feedback was communicated. More on this later when I attempt to tie all of this together.

Perception Management. Perception is reality to most people regardless of the facts. Anyone that is actively managing their career or personal life probably cares about perception. Furthermore, we probably want to be in control of how people perceive our actions, thoughts, attitudes and even mannerisms – lest it be established by others.

Total Quality Management. My current school studies are revolving around operations management. Specifically, quality improvement, TQM, Six Sigma, etc. There are concepts around TQM that can be applied to various dimensions of our lives: personal, professional, ethical, moral, giving, etc. Without going down a rabbit hole, I am convinced that quality improvement concepts allow us to construct guard rails (control limits) for the aforementioned dimensions.

So how does all of this tie together?

If you are serious about self-improvement and managing perception – you have to embrace feedback and take into consideration if you are approaching a quality limit if a feedback opportunity presents itself (me being the recipient). You may not agree with the merit of the feedback or agree with the delivery mechanism but you have to listen – just not hear – what is being communicated. This is really hard to do sometimes and how we react to the feedback experience can destroy relationships and further erode trust. When it comes to constructive criticism feedback – if someone is taking the time to give it – regardless of its validity – could this possibly be an indicator that we are approaching some of our quality limits – whether you have defined them or not?

For example, here are two commonly used rules for determining is a process is out of control:
1.    A single point outside the control limits.
2.    Obvious consistent or persistent patterns that suggest that there is something unusual about the data.

Keeping these two rules in mind, we can go through this exercise of introspection. Such an exercise requires one to put their pride on the shelf, set aside emotions, and really try to flush out the opportunity for self improvement. And, if all this can be done in a manner with a redemptive mindset – the better yet. In the end of such an exercise, there should always be one or more questions we should strive to answer:

1.    Is there something minor I can improve on? Is a slight adjustment needed to pull me back from the guard rails or better manage perception?
2.    Is there something major going on that calls for a massive adjustment? Is there really a fire that is producing all this feedback smoke?
3.    Was I a good partner in the feedback process? Did I listen? Did I have a redemptive mindset?

Hear me folks – this topic and what I have outlined is not something I consider myself to be a stellar example of. However, I do care about self-improvement, managing my perception, and adhering to quality in the execution of my responsibilities and will strive to keep in mind what I have outlined moving forward.

That’s it.

ABOUT BETAPERT. You can get very thorough explanations about  the betaPERT distribution here, here, and here. What follows is the ‘cliff notes’ version. The betaPERT distribution is often used for modeling subject matter expert estimates in scenarios where there is no data or not enough of it. The underlying distribution is the beta distribution (which is included in Microsoft Office Excel).  If we can over-simply and define a distribution as a collection or range of values – the betaPERT distribution when initially used with three values, such as minimum, most likely (think mode) and maximum values will create a distribution of values (output) that can then be used for statistical analysis and modeling. By introducing a fourth parameter – which I will refer to as confidence, regarding the ‘most likely’ estimate – we can account for the kurtosis – or peakedness – of the distribution.

WHO USES BETAPERT? There are a few professions and disciplines that leverage the betaPERT distribution:

Project Management – The project management profession is most often associated with betaPERT. PERT stands for Program (or Project) Evaluation and Review Technique. PERT was developed by the Navy and Booz-Allen-Hamilton back in the 1950’s (ref.1; see below ) – as part of the Polaris missile program. Anyway, it is often used today in project management for project / task planning and I believe it is covered as part of the PMP certification curriculum.

Risk Analysis / Modeling – There are some risk analysis scenarios where due to a lack of data, estimates are used to bring form to components of scenarios that factor into risk. The FAIR methodology – specifically some tools that leverage the FAIR methodology as applied to IT risk – is such an example of using betaPERT for risk analysis and risk modeling.

Ad-Hoc Analysis – There are many times where having access to a distribution like betaPERT is useful outside the disciplines listed above. For example, if a baker is looking to compare the price of her/his product with the rest of the market – data could be collected, a distribution created, and analysis could occur. Or, maybe a church is analyzing its year to year growth and wants to create a dynamic model that accounts for both probable growth and shrinkage – betaPERT can help with that as well.

OPENPERT ADD-IN FOR MICROSOFT OFFICE EXCEL. Jay and I developed the OpenPERT add-in as an alternative to paying money to leverage the betaPERT distribution. Of course, we underestimated the complexity of not only creating an Excel add-in but also working with the distribution itself and specific cases where divide by zero errors can occur. That said, we are very pleased with version 1.0 of OpenPERT and are excited about future enhancements as well as releasing examples of problem scenarios that are better understood with betaPERT analysis. Version 1.0 has been tested on Microsoft Office Excel 2007 and 2010; on both 32 bit and 64 bit Microsoft Windows operating systems. Version 1.0 of OpenPERT is not supported on ANY Microsoft Office for Mac products.

The project home of OpenPERT is here.

The downloads page is here. Even if you are familiar with the betaPERT distribution, please read the reference guide before installing and using the OpenPERT add-in.

Your feedback is welcome via support@openpert.org

Finally – On behalf of Jay and myself – a special thank you to members of the Society of Information Risk Analysts (SIRA) that helped test and provided feedback on the OpenPERT add-in. Find out more about SIRA here.

Ref. 1 – Malcolm, D. G., J. H. Roseboom, C. E. Clark, W. Fazar Application of a Technique for Research and Development Program Evaluation OPERATIONS RESEARCH Vol. 7, No. 5, September-October 1959, pp. 646-669

Following are a few Metricon 6 after-thoughts…

Venue: This was my first non-military trip to San Francisco. I loved the city! The vibe was awesome! The sheer number of people made for great people-watching entertainment and so many countries / cultures were represented everywhere I went. It gave a whole new meaning to America being a melting pot of the world.

Speakers: We had some great speakers at Metricon. Every speaker did well, the audience was engaged, and while questions were limited due to time – they took some tough questions and dealt with them appropriately.

Full list of speakers and presentations…

Favorite Sessions: Three of the 11 sessions stood out to me:

Jake Kouns – Cyber Insurance. I enjoyed this talk for a few reasons: a. it is an area of interest I have and b. the talk was easy to understand. I would characterize it as an overview of what cyber insurance is [should be] as well as some of the some of the nuances. Keeping in mind it was an overview – commercial insurance policies can be very complex – especially for large organizations. Some organizations do not buy separate “cyber insurance” policies – but utilize their existing policies to cover potential claims / liability arising from operational information technology failures or other scenarios. Overall – Jake is offering a unique product and while I would like to know more details – he appears to be well positioned in the cyber insurance product space.

Allison Miller / Itai Zukerman – Operationalizing Analytics. Alli and Itai went from 0 to 60 in about 5 seconds. They presented some work that brought together data collection, modeling and analysis- in less then 30 minutes. Itai was asked a question about the underlying analytical engine used – and he just nonchalantly replied ‘I wrote it in Java myself’ – like it was no big deal. That was hot.

Richard Lippman – Metrics for Continuous Network Monitoring. Richard gave us a glimpse of a real-time monitoring application; specifically, tracking un-trusted devices on protected subnets. The demo was very impressive and probably gave a few in the room some ‘metrigasms’ (I heard this phrase from @mrmeritology).

People: All the attendees and speakers were cordial and professional. By the end of the day – the sense of community was stronger then what we started with. A few quick shout-outs:

Behind-the-scenes contributors / organizers. The Usenix staff helped us out a lot over the last few months. We also had some help from Richard Baker who performed some site reconnaissance in an effort to determine video recording / video streaming capabilities – thank you sir. There were a few others that helped in selecting conference topics – you know who you are – thank you!

@chort0 and his lovely fiancé Meredith. They pointed some of us to some great establishments around Union Square. Good luck to the two of you as you go on this journey together.

@joshcorman. I had some great discussion with Josh. While we have only known each other for a few months – he has challenged me to think about questions [scenarios] that no one else is addressing.

+Wendy Nather. Consummate professional. Wendy and I have known of each other for a few years but never met in person prior to Metricon6. We had some great conversation; both professional and personal. She values human relationships and that is more important in my book then just the social networking aspect.

@alexhutton & @jayjacobs – yep – it rocked. Next… ?

All the attendees. Without attendance, there is no Metricon. The information sharing, hallway collaboration and presentation questions contributed greatly to the event. Thank you!

***

So there you go everyone! It was a great event! Keep your eyes and ears open for information about the next Metricon. Consider reanalyzing your favorite conferences and if you are looking for small, intimate and stimulating conferences – filled with thought leadership and progressive mindsets – give Metricon a chance!

BTW, I will be in San Francisco on August 9th and 10th for Metricon 6.

Been awhile since I publicly blogged. Between family, work, school, podcasting, helping run the Society of Information Risk Analysts (SIRA) and some public speaking – time has been limited. I want to briefly write about targets today.

I have had the privilege to speak twice in the month of May. The first engagement was at Secure360, an awesome regional information security conference based out of St. Paul, Minnesota. Mr. Jack Jones and I partnered up to give a talk about having a ‘seat at the table’. Specifically, speaking in a language that our IT and business leaders understand, establishing perspective, gaining influence, and providing value to our leaders so they can effectively manage risk. The talk appeared to be well received and there have been a few follow-up conversations with some information security professionals that want to up their game – which was the point to begin with.

Earlier this week I had the privilege to speak about IT risk management – specifically IT risk quantification – as part of the ‘CIO Practicum’ series at the University of Kentucky. The theme of this particular event was “Security for Grown-Ups”. I found myself in a room of IT and business executives who came to get a glimpse of how information risk management functions can begin adding value to the business or organization. My take-away from the event was that IT and business executives are craving value-add from information risk management functions (security, continuity management, compliance, etc.). Let me repeat in bold capital letters: IT AND BUSINESS EXECUTIVES ARE CRAVING VALUE FROM INFORMATION RISK MANAGEMENT FUNCTIONS.

So here is the dilemma. Information risk management professionals want to add value and our IT and business executives want [expect] value. How can we achieve goodness?

In order to achieve goodness, you and your leadership have to define it for your organization – you have to have a vision or a target to direct your efforts toward. It requires relationship building with your leadership and executives to develop a sense of mutual trust, perspective and shared understanding about why the organization exists, how the information risk management function fits into the organization as well as how the information risk management function contributes to helping the organization reach its goals and fulfill its strategy.

If you are an information risk practitioner, security, continuity management or compliance professional – what is the target that the outcomes of your efforts are directed towards? If you don’t know – figure it out quickly. Better yet – if your manager or other leaders cannot tell you then be proactive and work with your leadership to help define it.

If you are an IT or business executive that happened to stumble on this blog post – let me ask you a question. Have you established a vision or target for your information risk management function(s) to direct their efforts toward? If so – how is it working out? Is value being added? If a vision or target has not been established, why not?

Thoughts?

The issue was related to a gap with a 3rd party of which there was some Health Information Technology for Economic and Clinical Health Act (HITECH) fine exposure. The estimated HITECH fines were really LARGE. Large in the sense that the estimates:

a.    Did not pass the sniff test
b.    Could not be justified based off any documented fines / or statutes.
c.    From a simulation perspective were completely skewing the average simulated expected loss value for the scenario itself.

I reached out to better understand the rationale of the practitioner who performed the analysis and after some discussion we were in agreement that some additional analysis was warranted to accurately represent assumptions as well as refine loss magnitude estimates – especially for potential HITECH fines. About 10 minutes of additional information gathering yielded valuable information.

In a nutshell, the HITECH penalty structure is a tiered system that takes into consideration the nature of the data breach, the fine per violation and maximum amounts of fines for a given year. See below (the tier summary is from link # 2 at the bottom of this post; supported by links # 1 and 3):

Tier A is for violations in which the offender didn’t realize he or she violated the Act and would have handled the matter differently if he or she had. This results in a $100 fine for each violation, and the total imposed for such violations cannot exceed $25,000 for the calendar year.

Tier B is for violations due to reasonable cause, but not “willful neglect.” The result is a $1,000 fine for each violation, and the fines cannot exceed $100,000 for the calendar year.

Tier C is for violations due to willful neglect that the organization ultimately corrected. The result is a $10,000 fine for each violation, and the fines cannot exceed $250,000 for the calendar year.

Tier D is for violations of willful neglect that the organization did not correct. The result is a $50,000 fine for each violation, and the fines cannot exceed $1,500,000 for the calendar year.

Given this information and the nature of the control gap – one can quickly determine the penalty tier as well as estimate fine amounts. The opportunity cost to gather this additional information was minimal and the benefits of the additional analysis will result  in not only more accurate and defendable analysis – but also spare the risk practitioner from what would have been certain scrutiny from other IT risk leaders and possibly business partner allegations of IT Risk Management once again “crying wolf”.

Key Take-Away(s)

1.    Perform sniff tests on your analysis; have others review your analysis.
2.    There is probably more information then you realize about the problem space you are dealing with.
3.    Be able to defend assumptions and estimates that you make.
4.    Become the “expert” about the problem space not the repeater of information that may not be valid to begin with.

Links / References associated with this post:

1.    HIPAA Enforcement Rule ref. HITECH <- lots of legalese
2.    HITECH Summary <- less legalese
3.    HITECH Act scroll down to section 13410 for fine information <-lots of legalese
4.    Actual instance of a HITECH-related fine
5.    Interesting Record Loss Threshold Observation; Is 500 records the magic number?

In this post we want to combine the techniques demonstrated in parts two and three into a single simulation. To accomplish this simulation we will:

1.    Define input parameters
2.    Introduce VBA code – via a macro – that consumes the input parameters
3.    Perform functions within the VBA code
4.    Take the output from functions and store them in the spreadsheet
5.    Create a histogram of the simulation output.

Steps 3 & 4 will be performed many times; depending on the number of iterations we want to perform in our simulation.

You can download this spreadsheet to use as a reference throughout the post. The spreadsheet should be used in Excel only. The worksheets we are concerned with are:

test – This worksheet contains code that will step through each part of the loss magnitude potion of the simulation. By displaying this information, it allows you to validate that both the code and calculations are functioning as coded. This tab is also useful for testing code in small iterations. Thus, the number of iterations should be kept fairly low (“test”; B1).

prod – Unlike the “test” tab, this tab does not display the result of each loss magnitude calculation per iteration. This is the tab that you would want to run the full simulation on; thousands of iterations.

Here we go…and referencing the “prod” worksheet…

Input Parameters.
Expected Loss Frequency. It is assumed for this post that you have estimated or derived a most likely or average loss frequency value. Cell B2 contains this value. The value in this cell will be one of the input parameters into a POISSON probability distribution to return an inverse cumulative value (Part 2 of this Series).

Average Loss Magnitude. It is assumed for this post that you have estimated or derived a most likely or average loss magnitude value. Cell B3 contains this value. The value in this cell will be one of the input parameters into a NORMAL probability distribution to return an inverse cumulative value (Part 3 of this Series).

Loss Magnitude Standard Deviation. It is assumed for this post that you have estimated or derived the standard deviation for loss magnitude. Cell B4 contains this value. The value in this cell will be one of the input parameters into a NORMAL probability distribution to return an inverse cumulative value (Part 3 of this Series).

The Simulation.
On the “prod” tab, when you click the button labeled “Prod” – this will execute a macro composed of VBA code. I will let you explore the code on your own – it is fairly intuitive. I also left a few comments in the VBA so I remember what certain sections of the code are doing. There are four columns of simulation output that the macro will generate.

Iter# (B10). This is the iteration number. In cell B1 we set the number of iterations to be 5000. Thus, the VBA will cycle through a section of its code 5000 times.

LEF Random (C10). For each iteration, we will generate a random value between 0 and 1 to be used in generating a loss frequency value. Displaying the random value in the simulation is not necessary, but I prefer to see it so I can informally analyze the random values themselves and gauge the relationship between the random value and the inverse cumulative value in the next cell.

LEF Value (D10). For each iteration, we will use the random value we generated in the adjacent cell (column c), combine it with the Expected Loss Frequency value declared in B2 and input these values as parameters into a POISSON probability distribution that returns an inverse cumulative value. The value returned will be an integer – a whole number. Why a whole number? Because you can’t have half a loss event – just like a woman cannot be half pregnant ( <- one of my favorite analogies). This is a fairly important concept to realize from a loss event modeling perspective.

Loss Magnitude (E10). For each iteration, we will consume the value in the adjacent cell (column D) and apply logical rules to it.

a.    If the LEF Value = 0, then the loss magnitude is zero.
b.    If the LEF Value > 0, then for each instance of loss we will:
1.    Generate a random value
2.    Consume the average loss magnitude value in cell B3
3.    Consume the loss magnitude standard deviation in cell B4
4.    Use the values referenced in 1-3 as input parameters into a Normal probability distribution and return an inverse cumulative value. In other words, given a normal distribution with mean $2000 and standard deviation of $1000 – what is the value of that distribution point given a random value between 0 and 1.
5.    We will add all the instances of loss for that iteration and record the sum in column E.

Note: Steps 4 and 5 can be observed on the “test” worksheet by clicking the button labeled “test”.

The code will continue to loop until we have completed the number of iterations we specified in cell B1.

The Results. Now that the simulation is complete we can begin to analyze the output.

# of Iterations With No Loss (B5). This is the number of iterations where the returned inverse cumulative value was zero.

# of Iterations With Loss (B6). This is the number of iterations where the returned inverse cumulative value was greater than zero.

# of Loss Events (B7). This is the sum of loss events for all the iterations. There was some iteration where there was more then one loss event.

Max. # of Loss Events for an iteration (B8). This is the maximum number of loss events for any given iteration.

Next, let’s look at some of the simulation output in the context of loss severity; $.

Min. Loss (K6). This is minimum loss value returned from the simulation. I round the results to the nearest hundred in the worksheet.

Max. Loss (K7). This is maximum loss value returned from the simulation. I round the results to the nearest hundred in the worksheet.

Median (G5). This is the 50th percentile of the simulation results. In other words, 50% of the simulations results were equal to or less then this value.

Average (G6). This is the average loss value for the simulation. This is the quotient of summing all the loss magnitude values and dividing by the number of iterations. This value can quickly be compared to the median to make inferences about the skew of the simulation output.

80th % (G7). This is the 80th percentile of the simulation results. In other words, 80% of the simulations results were equal to or less then this value. In some industries, this is often referred to as the 1-in-5 loss.

90th % (G8). This is the 90th percentile of the simulation results. In other words, 90% of the simulations results were equal to or less then this value. In some industries, this is often referred to as the 1-in-10 loss.

95th % (G9). This is the 95th percentile of the simulation results. In other words, 95% of the simulations results were equal to or less then this value. In some industries, this is often referred to as the 1-in-20 loss.

99th % (G10). This is the 99th percentile of the simulation results. In other words, 99% of the simulations results were equal to or less then this value. In some industries, this is often referred to as the 1-in-100 loss.

Note 2: Generally speaking, the 95th, 99th and greater percentiles are often considered as being part of the tail of the loss distribution. I consider all the points in cells G5:G10 to be useful. For some loss exposures, the median and average values are more than enough to make informed decisions. For some loss exposures; the 80th, 90th, 95th, 99th and even larger percentiles are necessary.

Simulated Loss Magnitude Histogram. A histogram is a graphical representation showing the distribution of data. The histogram in the “prod” worksheet represents the distribution of data for all iterations where the loss was greater than zero.

Wrap Up. What I have presented in this post is a very simple model for a single loss exposure using randomness and probability distributions. Depending on your comfort level with VBA and creativity, one can easily build out more complex models; whether it is hundreds of loss exposures you want to model for or just a few dependent loss exposures.

If you are an information risk analyst or perform any type of IT risk analysis – you should really consider joining the Society of Information Risks Analysts mailing list. Over the last several weeks there have been some amazing exchanges of ideas, opinions, and spirited debate over the legitimacy and value of risk analysis. Some of the content is no doubt a pre-cursor to the anxiously awaited “Risk Management Smackdown” at RSA on 2/15. Regardless of my role within SIRA or the upcoming RSA debate, the SIRA mailing list is a great resource for learning more about IT risk analysis and IT risk management in general.

Below are a couple of links:

Society of Information Risk Analysts (SIRA)
Society of Information Risk Analysts – Mailing List
Society of Information Risk Analysts – Mailing List Archives (must be subscribed to the mailing list to view)
RSA Session Catalog (filter on security tag “risk management”)

I just finished reading The Communicators: Leadership in the Age of Crisis by Richard Levick and Charles Slack. For regular readers of this blog – you may recall a two part series back in 2009 on this blog – here and here – where Mr. Levick participated in a question and answer format on the topic of reputation risk. I have a lot of respect for the work Mr. Levick and his firm Levick Strategic Communications performs for their clients. “Why?” you might ask; the answer is risk management and leadership management.

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RISK MANAGEMENT

The majority of the readers of this blog have information risk management backgrounds. So I will speak to risk management first. I am going to define risk as the probable frequency and probable magnitude of future loss. For those familiar with the FAIR risk analysis methodology – specifically the taxonomy – you will recall that in the “loss magnitude” side of the taxonomy there are concepts such as “duration of loss”, “effect of loss” and “secondary stakeholders” that can inflict secondary loss against our company when a bad event occurs.

The Communicators is filled with examples about how an individual, business leaders, or organizations as a whole – can impact (both good and bad) the duration and effect of loss as well as effectively manage the perceptions of secondary stakeholders – when a bad event (or crisis) occurs. As risk practitioners, it is no longer acceptable to just know that a big loss event can impact our employer’s reputation or other more-tangible loss forms. We have to be able give real –yet practical – scenarios and examples of loss forms. Better yet, we need to offer additional value by asking tough questions that could shed light on a systemic weakness in existing plans to deal with a crisis when it does occur.

For the information risk practitioner, the following sections stood out to me:

Section 1: The Blind Spot. While this section is more about courage and leadership; there are time honored nuggets of wisdom in this section that we should embrace no matter what your role or title in the organization is.

Section 6: Leadership in the Digital Era. Social media is a double-edged sword – every information risk practitioner knows it. While social media can enable our company it can also be an information distribution mechanism that can damage our company’s reputation and ability to minimize loss in minutes compared to days, weeks or even months. Read this section to get great perspective on social media and the risks associated with it.

Note: With regards to the subject of risk management and its relationship with “bad” events. A crisis does not need to be initiated by something “bad” or an actual loss event. The Communicators gives a few examples of these scenarios (Rule #35; When Facts Don’t Matter, Forget The Facts).

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LEADERSHIP MANAGEMENT

As a former Marine, I cringe when I hear the words manager and leader used synonymously. Some organizations now even call all their managers “people leaders”. Philosophically, I can appreciate what is trying to be accomplished. But let’s face it there are managers out there that could not lead their teams out of an open door. I make such analogies to convey that leadership means something special to me. Thus, when I pick up a book that contains advice or examples of leadership – it better be good. The Communicators far exceeded my expectations.

If I was mentoring someone on the topic of leadership, using The Communicators as a mentoring aid and only had time to discuss one section; that section would be…

Section 9: Internal Leadership. The concept of ‘servant leadership’ is not necessarily new. Levick writes “Servant leadership defines the supervisory missions in terms of helping subordinates succeed and achieve through appreciation and reinforcement, not intimidation” (206). Just imagine a company where this approach was really embedded into its culture – not just a talking point on a PowerPoint slide deck that is helping your co-worker catch up on sleep and drool on him or herself. Better yet – forget about the manager / subordinate or corporate training aspect – what if everyone applied the concept of “servant leadership” in some or all aspects of their lives? Imagine how much more different our relationships and quality of life could be.

Leadership is not just about you and something you do relative to others. It is a mindset that can be leveraged at various levels of abstraction (personal, social, professional…) for those willing to embrace it.

In summary, I really enjoyed The Communicators and highly recommend it to anyone in the information risk management profession or anyone else that is serious about managing their career – regardless of your role or title.

Bene valete ac me vobis prodesse spero (“I bid farewell and hope I may help you”)