Useful Risk Modeling / Simulation Excel Add-Ons

October 18, 2010

I have had a lot of “modeling” and simulation conversations over the last several months. One common strand of these discussions is around Excel add-ons used to perform simulations and analysis. I want to share with you a few thoughts as well as some of the add-ons I use.

THOUGHTS

1.    Add-ons are great and they can make a lot of aspects of simulation and analysis “easy”. However, I would argue that you still need to understand “the why” and “the how” of features and functionality of the add-on to ensure you are using the add-on correctly. One of my modeling mentors discourages extensive use of “fancy” add-ons – he prefers plain vanilla Excel. He prefers native Excel because most of the add-ons are not worth the money. He has also suggested that if you are forced to write your own calculations and create your own model / charts, it forces you to learn the trade – especially the probability theory and statistics. There is truth in what he says but one has to balance time with learning.

2.    Generally speaking, most of what you need for Monte Carlo simulations is already in Excel. So, if you have the input parameters you need, know the right distribution to reference, and have some little to moderate VBA skills – you can begin to conquer the world.

3.    Add-on documentation. Any add-on or 3rd party plug-in should have ample documentation. In cases where the add-on includes its own probability distributions – understand why you would use one tool’s probability distribution implementation over any other.

TOOLS

1.    Microsoft Excel. At a minimum you want to use Excel 2007 – just to get around some of the row and column limitations of previous versions of Excel. Do not use versions of Excel prior to Excel 2003 as the random number generator was flawed. With Excel 2010, Microsoft included a new random number generator algorithm called Mersenne Twister. I won’t bore you with the details – just understand that it is a good thing and reduces the need for yet another Excel plug-in or 3rd party tool.

2.    SimTools. This is a recent find for me and it is FREE. The primary reason I like this add-in is for its broad implementation of inverse cumulative-probability functions that Excel 2007 does not have. Another reason I like this add-on is that the SimTool VBA code can be found here; just in case you need to validate calculations that are occurring or if your work is being audited / challenged.

3.    Simulacion. I was originally turned onto this add-on by my modeling mentor and it’s free as well; though there is a shortage of documentation. Some of the useful features are: distribution fitting and correlation capabilities. I do not use this add-on very much – but the distribution fitting capability alone makes it worth having.

4.    RiskAmp. I was introduced to RiskAmp via Risk Management Insight (RMI). Some of RMI’s tools leverage (require) the RiskAmp add-on. RiskAmp includes a few continuous probability distributions not included in Excel. One specifically is the betaPERT distribution. RiskAmp’s implementation of betaPERT allows for a fourth parameter called lamda which determines the height of the distribution. For what its worth, Oracle’s CrystalBall betaPert implementation does not allow for lamda. So, if betaPERT is a big deal in your analysis / simulations and you don’t need other flash-whiz-bang functionality out of Oracle ChrystalBall – save yourself over a $1000 dollars and purchase a copy of RiskAmp. One more thing on RiskAmp – I cannot prove it yet – but I think there are some memory sharing issues with their code. If you have multiple spreadsheets of which you are using RiskAmp for simulations – the speed of any one simulation is painfully slow.

5.    Mersenne Twister (MT) pseudorandom number generator. The nice folks at Structured Data (makers of RiskAmp) were nice enough to make a MT pseudorandom number generator add-on available for FREE. I have used this add-on in both Excel 2003 and Excel 2007. There is no need for it if you are using Excel 2010 – since Microsoft implemented the MT algorithm in its RAND() function.

6.    XLSim. This is a nifty Monte Carlo simulator that has a bunch of goodies in it that make it worth the couple of hundred dollars. There is a lot of flexibility with the chart output and the simulation menus are very intuitive. XLSim also includes the ability to store simulation results via the DIST standard. I will limit discussion of DIST for this post – but there are quite a few vendors that have already accommodated their software to create and/or work with DIST files.

7.    MegaStat. MegaStat came with one of my decision science textbooks while taking some courses at the Fisher College of Business; The Ohio State University. There is a great correlation matrix generator, the descriptive statistics functionality is great, and there are some other useful functions. While you can definitely find MegaStat for free on the Internet – consider buying a textbook from the creator of MegaStat, Professor J.B. Orris; Butler University. It’s ethically the right thing to do and it’s a small price to pay for a great utility!

8.    XY Chart Labler. This FREE Excel add-on has nothing to do with statistics or probability distributions. I use this add-on to add labels to data points for some charts. Does not sound like much but the functionality makes up for a shortcoming in Excel.

That’s it for now. Feel free to leave comments; especially if you have a really useful add-on or tool that you have used. I will mark as SPAM any comments that I feel are purely product pitches – with no substantive, experience based content.


ANALYTICAL THINKING or F.U.D?

August 28, 2010

Image Source; fudsec.com

SHORT BLOG POST VERSION:
I get a lot of satisfaction from teaching others the FAIR methodology. But equally satisfying is me knowing that I am helping build a culture of analytical thinking for both the class participant and our employer.

LONG BLOG POST VERSION:
This past week I had the privilege of teaching a three-day BASIC FAIR course at my employer. This is the second FAIR course I have taught and I can honestly state that I learned a lot about my company and the course participants; most of which I will be interacting with in the coming months in a consulting capacity.

Teaching the FAIR methodology is very challenging and rewarding. Because people’s preconceived notions of risk are challenged within minutes of being introduced to FAIR – there is no shortage of AH-HAH moments for them as well as no shortage of the instructor being stretched to unimaginable limits to take their examples and questions and view them through the lens of FAIR. I have walked away from both classes feeling like I learned more then they did.

I am currently reading “The Flaw of Averages” by Sam L. Savage. I highly recommend this book for a seasoned information risk practitioner. I will probably reference the book may times in future posts but for this post I want to talk about a sentence or two from Chapter 11; page 85 (hardcover). Savage references Well Fargo in 1997 and how they ‘maintained a culture of analytical thinking’.

So ask yourself this: Does my information risk management program instill a culture of analytical thinking or one of F.U.D. (Fear, Uncertainty & Doubt)?

The FAIR methodology when used correctly will force the practitioner to be analytical. But for an entire information risk management program to require all of its members to go through this training is telling of the culture we are creating. And guess what? This analytical thinking is not limited to our information risk management program. Our practitioners have to be able to explain their risk analysis to those individuals (IT & Business) accountable for the risk and responsible for the mitigation activity.

In summary, I get a lot of satisfaction from teaching others the FAIR methodology. But equally satisfying is me knowing that I am helping build a culture of analytical thinking for both the class participant and our employer.


Risktical Blog Series: SOTSOG

May 28, 2010

Recently in the information security web 2.0 circles there has been some buzz about “breaking” into the industry, what does career goodness look like, etc. This has prompted me to think about my journey to date; and I would like to write a series titled “Standing On The Shoulders Of Giants” (SOTSOG).

The first few posts in the series may have little to do with risk management since they date back to my childhood and Marine Corps days. However, it would be negligent of me not to reference some people or things dear to me from those days since it established the foundation from which I have been built upon.

For each post, I will make a point to highlight the one or two qualities from that particular “giant” that I think applies to the information risk management professional. And just in case I forget to write the following points in my posts let me take the opportunity to do it now:

1. YOU are responsible for YOUR career (period).
2. YOU cannot control EVERY aspect of YOUR career.
3. However, YOU are responsible for how YOU deal with the things YOU cannot CONTROL
4. Its OK to admit YOU don’t know something
5. The advice I give to kids going into boot camp or learning something brand new: KEEP YOUR EYES AND EARS OPEN and YOUR MOUTH SHUT!

I hope you will enjoy the series. I will try not to expose too much of the gooey center inside my crunchy shell.


More Heat Map Love

May 11, 2010

In my previous post “Heat Map Love” I attempted to illustrate the relationship between plots on a heat map and a loss distribution. In this post I am going to illustrate another method to show the relationship – hopefully in simpler terms.

In the heat map above I have plotted five example risk issues:

I: Application security; cross-site scripting; external facing application(s); actual loss events between 2-10 times a year; low magnitude per event – less then $10,000.

II: Data confidentiality; lost / stolen mobile device or computer; no hard disk encryption; simulated or actual one loss event per year, low to moderate magnitude per event.

III:  PCI-DSS Compliance; level 2 Visa merchant; not compliant with numerous PCI-DSS standards; merchant self-reports not being in compliance this year; merchant expects monthly fines of $5,000 for a one year total of $60,000.

IV: Malware outbreak; large malware outbreak (greater then 10% of your protected endpoints). Less then once every ten years; magnitude per event could range between $100,000 and $1,000,000; productivity hit, external consulting, etc.

V: Availability; loss of data center; very low frequency; very large magnitude per event.

Since there is a frequency and magnitude of loss associated with each of these issues we can conceptually associate these issues with a loss distribution (assuming that our loss distribution is a normal-like or log normal).

Step 1: Hold a piece of paper with the heat map looking like the image below:

Step 2: Flip the paper towards you so the heat map looks like image below (flip vertical):


Step 3: Rotate the paper counter-clockwise 90 degrees; it should like the image below.


For ease of illustration; let’s overlay a log normal distribution.

What we see is in line with what we discussed in the “Heat Map Love” post:

Risk V – Loss of data center; is driving the tail; very low frequency; very large magnitude.
Risk IV – Malware outbreak; low frequency; but significant or high magnitude.
Risk III – Annual PCI fines from Visa via acquirer / processor; once per year; $60K.
Risk II – Lost or stolen laptop that had confidential information on it; response and notification costs not expected to be significant.
Risk I – Lots of small application security issues; for example cross site scripting; numerous detected and reported instances per year; low cost per event.

There you have it – a less technical way to perform a sniff test on your heat map plots and / or validate against a loss distribution.

Once you have taught everyone how to perform this artwork paper rotation trick. You can have a paper airplane flying contest.


Heat Map Love

May 6, 2010

First, I would like to welcome Jack Jones *back* to the world of risk blogging. Jack blogged a few weeks ago on the subject of heat maps; “Lipstick on Pigs” and “Lipstick Part II”; prompting a response by Jared Pfost of Third Defense. These are great posts that underscore the need to structure and leverage heat maps in an effective and defensible manner.

The purpose of this post is to share a recent “ah hah” moment involving heat maps and loss distributions. Whether you are an advocate or not of risk quantification or simulation modeling – it is hard to criticize one for having tools or procedures in place that essentially serve as a “risk sniff test”. I consider reconciling portions of a loss distribution to a heat map – a pretty useful sniff test.

QUESTION TO BE ANSWERED: How do I reconcile – or validate – the plotting of a heat map bubble with a loss distribution?

Well, it depends…but let’s establish some context.

•    5-by-5 heat map. The X axis of the heat map represents “Frequency of Loss”; the Y axis represents “Magnitude of Loss”. Each axis is broken into 5 sections.

•    Let’ say we have a heat map whose bubbles represent categories of risk issues (ISO 27002 categories, BASEL II OpRisk Categories, etc…).

•    At a minimum, all of these issues have been assessed with some methodology and/or tool (I prefer FAIR) that allow us to associate the frequency and magnitude of loss for each and every issue.

•    We can perform thousands of simulation iterations for each and every issue in the risk repository, perform analysis, determine categories of risk that are contributing the most to various percentiles of the loss distribution, and then associate them with a heat map.

•    For the purpose of this post we are going to make a good faith assumption that our loss distribution resembles a log-normal or normal “like” distribution.

Back in my “Rainbow Risk” post I shared an example of a “rainbow chart”; a 100% stacked bar chart representing the contribution of loss that a category contributes (by percentage) to any given loss distribution percentile. For example, in the rainbow chart on that post, it showed that Business Continuity Mgmt category of risk issues accounted for about 55% of the risk in the 99th percentile. On a heat map, most significant IT Business Continuity issues are probably going to be very low frequency, very high magnitude events. Thus, it is fairly intuitive that very low frequency / very high frequency magnitude loss events would “drive” the tail of a given loss distribution.

In the images below, I have mapped areas on a heat map (image 1) to areas on a distribution (image 2). Specifically, I am trying to illustrate how frequency and magnitude for any given issue factors into or most likely represented in a loss distribution.

Image 1
Image 2

Area A – Very low frequency, very high magnitude risk issues. These types of events or risk issues drive the tail portion of a loss distribution.

Area B – Very low frequency, moderate or high magnitude risk issues; or low to moderate frequency, very high magnitude loss events. It can be said that these type of issues also drive the tail – but maybe not as much past the 99th percentile like issues associated with Area A.

Area C – Low to Moderate frequency, moderate or high magnitude.  These issues are best represented in the middle of the distribution; generally speaking, around one standard deviation on both sides of the mean.

Area D – Very frequent, moderate or high magnitude. Loss associated with these issues is not as severe as those of Areas A and B; but are typically greater then the mean expected loss.

Area E – Very frequent, very high magnitude. Generally speaking, these issues probably drive the portion of the distribution between 1 and 2.5 standard deviations (to the right of the mean).

Area F – Low or moderate frequency, low or moderate magnitude. These issues best factor into the area of the distribution left of the mean. Loss associated with these issues is less then the mean.

In closing, I would share at least one use case for performing this analysis or validation. Key risk heat maps. If all of your issues have frequency and magnitude values as well as some other attributes associated with the issue, you can:

1.    Perform simulations on all of these issues.
2.    Calculate their contributions to various distribution percentiles
3.    Analyze the results by various attributes (ISO 27002, BASEL II, IT Process, etc…).
4.    Chart derived information (categories of risk) on a heat map
5.    Review for plausibility / accuracy (this should occur all the time)

I welcome any feedback!


Rainbow Risk

April 1, 2010

One of the benefits of quantifying risk for an information risk issue or finding is that it gives us an additional dimension for aggregate risk analysis. Instead of just simple counts of issues categorized by ISO 27002 section – we can now analyze risk (dollar values) in numerous contexts; limited primarily by the data you are collecting and your ability to associate some data elements to various frameworks. (ISO 27002, BASEL II, COBIT IT Processes, etc…).

Loss simulation and aggregate risk analysis is a big part of my world right now. However, one of the most complex challenges I face is visually representing risk in a meaningful manner to management. So, over the next few posts, I want to share some visualizations of risk and how they could possibly be used for decision making.

The chart above is often referred to as a “rainbow chart”. Technically speaking though, it is a 100% Stacked Bar Chart. What this chart is depicting is the breakdown of risk (percentage of loss and ISO 27002 section) of a simulated aggregate loss distribution; by loss distribution percentile. Say what…?

Let’s break it down…

Warning – I am going to oversimplify some statistical concepts. And yes – all of the percentages and dollar values are from a dataset with fictitious data for the purposes of illustration.

Output from simulations are often analyzed by percentiles. We often zero in on the 50th percentile; sometimes an indicator of the mean or expected loss for a defined time frame. For illustration purposes let’s associate some dollar values to loss distribution percentiles:

5th percentile: $100K
50th percentile: $1M
80th percentile: $2M
99th percentile: $5M

Given the dollar values above as well as the chart we can infer the following:

5th percentile:
a.    5% of the iterations resulted in simulated loss of around $100K.
b.    Roughly 30% of the simulated loss at the 5th percentile is related to Compliance issues (~33%; $33K).
c.    Another 30% of the simulated loss at the 5th percentile is related to Access Control issues (~30%; $30K).
d.    About 20% of the simulated loss at the 5th percentile is related to Systems Development & Maintenance issues (~20%; $20K).
e.    About 8% of the simulated loss at the 5th percentile is related to Business Continuity Mgmt issues (~8%; $8K).

50th percentile:
a.    50% of the iterations resulted in simulated loss of around $1M.
b.    Roughly 28% of the simulated loss at the 50th percentile is related to Compliance issues (~28%; $280K).
c.    Another 30% of the simulated loss at the 50th percentile is related to Access Control issues (~30%; $300K).
d.    About 22% of the simulated loss at the 50th percentile is related to Systems Development & Maintenance issues (~22%; $220K).
e.    About 12% of the simulated loss at the 50th percentile is related to Business Continuity Mgmt issues (~12%; $120K).

99th percentile:
a.    1% of the iterations resulted in simulated loss of around $5M (100% -99% = 1%).
b.    Roughly 12% of the simulated loss at the 99th percentile is related to Compliance issues (~12%; $600K).
c.    Another 15% of the simulated loss at the 99th percentile is related to Access Control issues (~15%; $750K).
d.    About 12% of the simulated loss at the 99th percentile is related to Systems Development & Maintenance issues (~12%; $600K).
e.    About 55% of the simulated loss at the 99th percentile is related to Business Continuity Mgmt issues (~55%; $2.75M).

PRACTICAL APPLICATION

This type of risk visualization allows for the following:

1.    BETTER VISIBILITY. Gives us visibility to risk by loss severity and by ISO 27002 sections.
2.    INFORMED DECISIONS. Allows for tactical and strategic decision making.
a.    Tactical. Risk around the 50th percentile is loss we would expect 50% of the time; time being annually. Thus, we can actively manage (reduce or maintain) expected annual loss by targeting categories of risk at this or surrounding percentiles.
b.    Strategic. Risk around the 80th (1-in-5) or 90th (1-in-10) percentiles is greater in severity but less likely to occur. However when you think of this in terms of years and given the volatility of threats in our profession – we have to be mindful of low frequency / high magnitude loss events. Thus, to address the risk at these percentiles – we could be more strategic in our planning and investments. It could take a few years to mitigate the risk down to an acceptable level – but we can spread those mitigation costs over time.
3.    COST / BENEFIT ANALYSIS. Above I listed risk at the 80th percentile to be about $2M dollars. We can quickly see that 30% of the risk at the 80th percentile is related to Access Control (~30%, $600K). If I want to reduce the access control related risk at this percentile and I estimate it’s going to cost me $100K – I can demonstrate a risk reduction both mathematically and by re-running my simulations absent issues that would be mitigated by my investment.
4.    DRILL DOWN. Depending on how your issues are tagged, we could drill down into ISO 27002 sections to see which sub-sections are driving the most risk for its parent section.

FINAL THOUGHT. There is uncertainty with risk. Performing aggregate analysis and simulations on your entire risk repository highlights the variability of loss and can make it easier to explain uncertainty to others outside the information security profession. Take for example the “compliance risk” values above. Annual expected loss associated with Compliance-related issues could be as little as $33K, most likely $280K and possibly $600K or worse. Do not underestimate management’s ability to understand this uncertainty and their appetite to make effective decisions around it.


Working With External Data (Part 2 of X)

February 2, 2010

This is the second post of a series related to working with external data for analysis or modeling purposes. You can read the first post HERE or read the “cliff notes” summary below.

***

Part 1 “Cliff Notes”

1.    Know what information you are hoping to derive from the data.
2.    Methodically narrow down your data for relevant data points. More data does not guarantee better or more accurate information.
3.    Some refinement considerations include:
a.    Time frames. Limit your data set to a span of time commensurate with a minimum level of technology as well as a consistent expectation of regulatory / industry standard requirements.
b.    Good fit. Consider points related to your industry, service offering / value proposition, and loss form categories.
c.    Duplicate records. When working with multiple external data sources, keep an eye out for duplicate event records.
d.    Consistency. Be consistent in how you analyze data points.

NOTE: Collecting and right sizing external data is useful for comparing your internal loss events to external loss events, understanding what a worst-case loss could like for your company and possibly incorporating into a data set to be used for modeling purposes.

***

In this post, I want to focus on right-sizing data points in your data set commiserate with the size of your company.

1.     Determine the minimum value where right-sizing is not worth the effort. When faced with hundreds or even thousands of data points – there is going to be a “magic number” of records where the number of records lost does not warrant right sizing. The more you understand your business processes and partner with the appropriate stakeholder (business partners, marketing, legal, privacy, etc…) the easier making this determination should be. They *should* be the subject matter expert(s) on these matters and be leveraged whenever possible.

TIME-OUT. For the uber-privacy / legal folks our there, the loss of just one record is not desirable. But, we have to be reasonable and acknowledge that are thresholds where the “squirm factor” varies.

2.    Understand the type of data lost / compromised. This is really easy to overlook. Some data loss events involve just customer (consumer) data, other events may include just employee data, and some events may include both types of data. Understanding the type of data lost could prove useful in determining which right-sizing method to use.

3.    Right-sizing factors (proportions). This is where things get interesting. It also where objectivity and consistency have to be demonstrated. Whether we are performing risk assessments, right-sizing data points, or collecting information to draw conclusions from – it is important that we are as objective as possible; reducing subjectivity whenever possible. The key point I want to make here is that if used appropriately and consistently, a right-sizing exercise is more objective in nature then stating it happened to company X so it could happen to us without any analysis whatsoever. You may want to make a brief note as to why you chose a certain right-sizing factor in case you need reminded at a later point for whatever reasons. Let’s look at a few right-sizing options (keep in mind that we are building upon what we covered in the first post):

a.    # of Employees. If a data point is from a company in the same industry or has the same value proposition – using number of employees could be a good right-sizing factor. Some inferences can be made around the number of employees. Is it unreasonable that if a company half of your size loses 10,000 records and is obligated to protect data equally as well as your company, that the same event could happen to your company for 20,000 records? Using number of employees is also useful when the data point only involves confidential employee data.

b.    Revenue. Revenue could be another right-sizing factor. Maybe the data point is from a different industry where staffing types / level differ from your industry but the value proposition is related (property & casualty insurance versus health insurance).

c.    Equity. In some cases, equity can be used as a right-sizing data point. # of employees or revenue may not be appropriate or the proportions could be unrealistic. Equity could be a third option.

d.    Others. There could be some other right sizing factors depending on your industry or the problem you need to make decisions for. Just make sure that whatever that factor is, that it is generally regarded as a sound comparative measurement factor and document any assumptions. Again, I cannot underscore the need for objectivity and consistency.

NOTE: Keep an eye out for some data points that have been right-sized but the calculated value far exceeds the total number of records you have in your organization. Consider another right-sizing factor or change to the maximum amount of records you have; and of course, document. Some situations may warrant keeping the value (like a risk assessment related to merger or acquisition where the number of records you are obligated to protect now- doubles, triples, etc…). What you don’t want is someone calling you out on a data point that is not even realistic for your organization because of a simple oversight (I speak from experience).

4.    Sources of Information. In order to right-size data points – especially in the context of the factors above (employees, revenue, equity), you have to get information about the companies related to that external data point. I would submit that the information you need is available in most of the cases – it is just a matter of time and creativity.

a.    Internal information. You need to collect your own internal information for the right sizing factors before you can right-size against external data point company information. HR may be able to give you # of employees by year and hopefully there are numerous internal authoritative resources that have your revenue and equity values (if you are publically traded – this information is publically available; though you should still confer / validate with internal sources).

b.    External information. Be Creative. Yahoo business, Dun & Bradstreet, company name Google searches combined with “annual report” or “corporate filings”, company websites and Fortune 100, 500 or 1000 lists; these are stating points. Just remember to make sure you are right-sizing in the context of the same year the incident occurred in – and be consistent.

In the next post for this series, we will look at analyzing a right-sized data set to begin collecting information. For example, does the data resemble a statistical model? Does it resemble your internal data points? What if my data set has too few data points?


What’s In Your Wallet?

December 28, 2009

A few weeks back I jumped feet first into a blog post at Securosis by David Mortman titled “Changing The Game”. There are a lot of comments but one comment in particular by Rich Mogull has resulted in me doing some soul searching, adding a new question to my bank of interview questions, and forcing me to write a blog post (while on Christmas / New Year’s vacation).

Below is the majority of the comment that Rich made:

“The problem I think we have in infosec is that the economics are skewed to distort risk analysis (see my post on the anonymization of losses), and we fundamentally lack the proper data to make truly informed risk decisions.

I do think we are creeping slowly in the right direction- the Verizon report is one example on the data front, and it’s the main reason we are focusing so much on metrics models.

One area where I do think we need to be cautious is the need in many financial and insurance models to tie everything to monetary value. Since “loss” has a different meaning in the digital world due to us usually not losing access to the asset as with physical loss, the models don’t fully translate.”

So here is my question to you as a reader: What Is Your Information Risk Management Philosophy in regards to risk quantification? Do you even have one?

There is a lot of skepticism in our industry – sometimes packaged as healthy scrutiny – when it comes to the topic of risk quantification and tying loss forms to monetary values. Below are some of my “philosophical” thoughts about Information Risk Management specifically as it pertains to risk quantification.

1.    Security Events / Incidents Have An Opportunity Cost. When something “bad” occurs – it costs the company money to respond. Whether it is “green dollars” going out the door or soft dollars associated with the hourly cost of full time employees responding to the event, the reality is that the company will deal with the incident and that response effort usually takes away from other responsibilities or objectives. We can count green dollars, but counting the internal costs can be more challenging; the size and maturity of the HR/IT organization will factor into the ease of doing this. Bottom line: It costs money.

2.    It Costs Money to Maintain a Security Posture. One of the executives at my company referred to this concept as “anchoring costs”. A perfect example of this is malware protection. A company may spend $125,000 dollars a year in malware maintenance / support fees; a solution that is considered to be 96% effective against malware in the wild with advanced detection / heuristic capabilities.  For simple illustration purposes, let’s state that there are two full-time employees on the malware team ($50K each) – that’s an additional (minimum, excluding benefits, etc..) $100K on top of the $125K to manage, maintain, and support a malware protection capability; a grand total of $225K per year. This is an example of an anchoring cost: the company is spending $225K a year to protect against a malware outbreak or event that could result in loss of productivity – i.e. deliver its value proposition – or prevent data theft / compromise. We could probably spend a few days debating if this particular anchoring cost accounts for the expected amount we would lose in a given year without malware protection or if this annual anchoring cost is to address a risk value further out in a loss distribution (1-in20; 80th percentile, 1-in-100; 99th percentile). Bottom line: It costs money to maintain a security posture.

3.    Overcapitalization. Now we are moving into the ERM space – and this concept may be limited in scope from an industry perspective – but it is evolving and can facilitate decision making in some organizations. Economic capital models account for various types of risk. One of those risk types is operational risk – of which information security and continuity management risks fall under. Below is a broad definition of economic capital (Wikipedia):

“Economic capital is the amount of risk capital, assessed on a realistic basis, which a firm requires to cover the risks that it is running or collecting as a going concern, such as market risk, credit risk, and operational risk.” (BTW, I really like the phrase “assessed on a realistic basis”)

One analogy I read on overcapitalization in the last few days was comparing overcapitalization to an overweight person. Too much weight can lead to health problems and other challenges. In addition, the extra weight inhibits our flexibility and speed.

Assuming that you are quantifying risk issues, and assuming that these data points can be rolled up into an economic capital model – it is clear that risk quantification for the information security / continuity management issues we manage- can contribute to enterprise risk management. I think an argument can be made – especially in the insurance industry – that company leadership has much more opportunity and influence to manage (reduce) operational risk – then other risk types, for example weather / catastrophe risk. Yes, operational risk is probably a very small percentage of economic capital. However, the higher the economic capital amount – the higher cost to the company to maintain that amount and it could reduce their ability to use some of that money for other purposes. In addition, regardless if operational risk is only a tiny percentage of economic capital models – the margin of difference between competing products and competitors in the market place is sometimes so small that reducing just a small percentage of expenses or operational risk – could result in some form of competitive advantage (product pricing, investments, expansion, etc..).

Bottom line: I would rather be contributing to our business in a strategic manner using words, concepts and measurement methods  they are familiar with, versus some qualitative approach that does not lend itself to effective decision making.

4.    Motives. Given the current economic climate, a lot of people (infosec professionals, infosec executives, friends, relatives, etc..) are skeptical of risk models. I understand why. Here is how I professionally reconcile such concerns / skepticisms.

a.    Apples and Oranges. Economic capital models ( and at a smaller level – risk issue quantification) and investment models have different purposes. The former is about ensuring a company can covers its liabilities. The latter – in most cases – is about opportunity – profit.

b.    Motives. I think you have to look at the motives of companies or individuals that are attempting to quantify information security / continuity management risk. What they are trying to do is ensure that their company understands their exposure in the information risk management space. This is where the phrase “assessed on a realistic basis” comes back to mind. Is a sound and repeatable risk assessment methodology being used consistently to assess risks? Are loss forms that are being estimated best case, most likely loss, worse case loss or a combination (distribution) of the three? Are we packaging information that allows effective decision making, or are we “crying wolf” and packaging scare tactics? In most cases, information risk management groups are just trying to give the best information. Yes, there will be misses in either frequency of loss or magnitude of loss – but that is the nature of risk.

So there you have it, some of my thoughts on risk quantification and why I support it passionately. Ask yourself, “Can I defend why I am passionate about my favorite aspect of information risk management?” If not, I challenge you to go through the thought exercises.  I welcome your feedback.

Happy Holidays!


Reputation Risk: Some Additional Thoughts

August 8, 2009

Thinking_Person

This is a follow-up post to the two part Richard Levick “reputation risk” series. The related posts are here: part 1, part 2, and some additional thoughts from Richard.

Below are my thoughts regarding some information and advice that Richard shared with us.

3.    What are the key components of a reputation?
Levick: … So the first rule is “Understand your reputation.”… If you don’t understand it, you can’t protect it.

This sounds like an absolute no-brainer statement but I cannot underscore how important this is for information security practitioners, especially those performing risk assessments. I have stated it elsewhere on my blog; we are in a unique position to truly gap the IT and business divide. Providing relevant business context to our leaders for the issues we want them to care about and respond to – is value for them and the company as a whole. In addition, this is more then just knowing buzzwords and when to drop them. We need to present ourselves as an authoritative reputation stakeholder when we talk about reputation risk to our managers and business leaders.

4.    How can reputation be impacted when there are IT security incidents?
Levick: … The issue is how the company behaves once a data breach is discovered….

So much can be written about this part of Richard’s answer; but let’s talk about this in the context of security controls. Generally speaking, there are three categories of security controls: preventive, detective, and response. So when it comes to reputation risk, I immediately try to consider what response controls my company has at its disposal to respond to a security incident that has the potential to be known outside our company.

There are two response controls that immediately come to mind (they could be called various things):

Communications Plan: Does your company have a communications plan? Does the communication plan take into account data loss or network breach scenarios? The questions are numerous….

Event Management Plan: Does your company or information security organization have an event management plan? How thorough is it? Does it tie into your communication plan? Do the right players in your company have a role in the event management plan? Again, a lot of things to consider.

Bottom line: The effectiveness of the response controls listed above can significantly factor into the magnitude of reputation risk. Now, when you factor in how and what is being communicated – that may be beyond your control – but I would challenge you to see these plans for yourself so when you estimate or articulate reputation risk – you are doing so with conviction and some level of confidence.

Finally, not everyone reading this may work for a large company that has a robust event management plan or a communication plan; let alone any plans at all. My advice, initiate the conversation and see it where it takes you or your management!

***

Something I heard while serving in the U.S. Marine Corps that has proved so valuable over the years is this: It is better to be tried by twelve then carried by six. Meaning, when faced with an opportunity to make a decision, escalate a situation, share information, or ask questions – it is better to do so NOW – and face ridicule / judgment – then do nothing at all. Take it for what it is worth…

***

5.    Can reputation be measured or quantified in units of dollars?

I agree that precisely measuring reputation in terms of dollars is challenging at best – but you can still perform some level of measurement. Generally speaking, reputation risk comes into play as a secondary loss form. Meaning, that certain incident information is known outside the company by someone that can be considered a stakeholder of our company (consumer, customer, government, etc…). A security incident could result in loss of customers, decreased sales, fines and judgments, class action law suits, negative publicity, etc…; most of which can be tied back to dollar values – and associated with reputation risk. Even if you disagree with this approach, if you are dealing with risk issues where reputation risk is a legitimate loss form, you can articulate that reputation risk is a contributing factor to the overall loss magnitude. Finally, I would caution using reputation risk as the FUD stick that Jack Jones mentions in a comment in post 2; but make sure your audience understands that you think reputation is an important part of the overall exposure; document it as well.

I hope you enjoyed the series. Have a splendid day!


Reputation Risk Q&A – Richard Levick (2 of 2)

August 6, 2009

reputation-balloon

This is part two of a reputation risk Q&A with Mr. Richard Levick; President and CEO of Levick Strategic Communications in Washington, DC.

Part one can be found here.

6. In your opinion, how do you distinguish between worst-case reputation loss versus expected reputation loss?

Richard Levick: One word – experience. That’s how you anticipate what’s coming next and prevent the worst-case scenario from coming to fruition. It’s all about staying one step ahead.

Today, the period of time between the gating event that alerts you to a brand crisis and the bet-the-company moment is increasingly indistinguishable. When video of two Domino’s employees defiling customers’ food was posted to YouTube earlier this year, one million people – a number greater than those who subscribe to The New York Times or The Wall Street Journal – had viewed it within the first 48 hours. What that tells us is that crises now move faster than ever before and that companies have to be ready to act at moment’s notice. That means preventing and responding to reputational risks and crisis needs to be in the DNA. You don’t get that by accident. Or maybe you do, but at a terribly high price.

To do it right and prepare ahead of time means knowing what regulators, Congress, or state attorneys generals are going to do next. It means anticipating the next moves of the plaintiffs’ bar. It means monitoring the blogosphere and other social and digital media for intelligence as to where the traditional media may soon be heading. It means identifying likely company risks now and extrapolating what this means in terms of Search Engine Optimization, High Authority Bloggers, and social media. If you are reading this last sentence and don’t understand what I mean, your company is at far greater risk than you think.

To get started, build a relationship with crisis managers now – before you need them – so that you can build the trust that fast action demands. In crisis, you’ve got to see how the dominos – no pun intended – are lined up and know how they’re going to fall. It’s the only way to keep up with a news cycle that is now measured in minutes, not hours.

7. What are the key controls an information security risk analyst should take into consideration when assessing reputation loss impact (or magnitude)?

Richard Levick: With virtually every traditional journalists now regularly reading blogs for story ideas, careful monitoring of the blogosphere provides invaluable intelligence as to the scope of the reputational damage that may result from IT security breach.

That means knowing the high-authority bloggers – those with the greatest influence over perceptions – that cover your industry. And it also means being ready to engage them should a data breach occur. By bringing bloggers into the fold, companies allow themselves an opportunity to shape the narrative before it influences the traditional commentary to follow – and thus limit the reputational damage potential at play.

8. Do you have any tips for effectively communicating reputation risk to middle management and executive leadership?

Richard Levick: In today’s media environment, the C-Suite has to know that everything it does – or chooses not to do – can potentially impact the corporate brand. That means always thinking like your consumers, investors, regulators, and stakeholders that run the gamut – and taking their perceptions into consideration whenever a decision that could potentially impact these audiences is made.

I think middle managers need to own issues like understanding who the High Authority Bloggers are and having personal relationships; anticipating risks and knowing who controls those terms on the search engines; tracking YouTube, Twitter, and other sites for signs of consumer or stockholder dissatisfaction or industry unrest; and recommending instant positive intervention. Middle managers need to think differently. Today is a good day to start.

9. Do you have a favorite reputation risk engagement that you are willing to share (regardless of outcome)?

Richard Levick: I often look back to what Hasbro did during the 2007 lead-paint scare because it demonstrates how a crisis can be transformed into opportunity if a company articulates leadership in solving the problems at hand.

While Hasbro did not initiate a single recall during the lead paint crisis, the company recognized that its entire industry was under siege. Inaction could have led to guilt by association in the Court of Public Opinion. More important, remaining on the sidelines could have allowed a significant opportunity to differentiate itself from the competition to slip by.

So, rather than sit back and let the competition take the heat, Hasbro stepped up by implementing a “Total Safety Program” and making the initiative a central element of its traditional and online marketing strategies. As a result, the company became the “gold standard” around which all of its competitors were forced to rally. Though it wasn’t directly impacted by the crisis, Hasbro took action to abate it. As a result, its October 2007 earnings jumped 64 percent from the previous year.

10. Are there any good sources of information you can recommend for learning more about this subject?

Richard Levick: I would point to four such resources maintained by my firm…

Levick Strategic Communications’ Bulletproof Blog™ (www.bulletproofblog.com)…

Our e-newsletter, High Stakes™ (http://www.levick.com/resources/highstakes/)…

Our Crisis Communications Desk Reference (http://www.levick.com/crisis_communications_desktop_reference/)…

And our book, Stop The Presses (http://www.levick.com/resources/books/stop_the_presses/).

Also, I would encourage your readers to keep an eye out for our next book, on leadership during crisis in the digital age, which will be coming out in early 2010.

***

I intend on posting some of my thoughts on Richard’s answers in an upcoming post. I hope you found Mr. Levick’s perspective to be as useful and intriguing as I do. Regardless, thank you Richard for participating in this effort; I look forward to continued interactions.


Follow

Get every new post delivered to your Inbox.