An Introduction to Risk Analysis

Exclusive offer: get 50% off this eBook here
Enterprise Security: A Data-Centric Approach to Securing the Enterprise

Enterprise Security: A Data-Centric Approach to Securing the Enterprise — Save 50%

A guide to applying data-centric security concepts for securing enterprise data to enable an agile enterprise with this book and ebook.

$26.99    $13.50
by Aaron Woody | June 2013 | Architecture & Analysis Enterprise Articles

Security is a process that requires the integration of security into business processes to ensure enterprise risk is minimized to an acceptable level. Security as a process is an approach that highlights the integration of security and business initiatives to reduce the security impact of implementations and changes to the enterprise environment. This article by Aaron Woody, author of Enterprise Security: A Data-Centric Approach to Securing the Enterprise, will introduce the concept of using risk analysis to drive security decisions.

(For more resources related to this topic, see here.)

Risk analysis

First, we must understand what risk is, how it is calculated, and then implement a solution to mitigate or reduce the calculated risk. At this point in the process of developing agile security architecture, we have already defined our data. The following sections assume we know what the data is, just not the true impact to the enterprise if a threat is realized.

What is risk analysis?

Simply stated, risk analysis is the process of assessing the components of risk; threats, impact, and probability as it relates to an asset, in our case enterprise data. To ascertain risk, the probability of impact to enterprise data must first be calculated. A simple risk analysis output may be the decision to spend capital to protect an asset based on value of the asset and the scope of impact if the risk is not mitigated. This is the most general form of risk analysis, and there are several methods that can be applied to produce a meaningful output. Risk analysis is directly impacted by the maturity of the organization in terms of being able to show value to the enterprise as a whole and understanding the applied risk methodology. If the enterprise does not have a formal risk analysis capability, it will be difficult for the security team to use this method to properly implement security architecture for enterprise initiatives. Without this capability, the enterprise will either spend on the products with the best marketing, or not spend at all. Let's take a closer look at the risk analysis components and figure out where useful analysis data can be obtained.

Assessing threats

First, we must define what a threat is in order to identify probable threats. It may be difficult to determine threats to the enterprise data if this analysis has never been completed. A threat is anything that can act negatively towards the enterprise assets. It may be a person, virus, malware, or a natural disaster. Due to the broad scope of threats, actions may be purposeful or unintentional in nature adding to the absolute unpredictability of impact. Once a threat is defined, the attributes of threats must be identified and documented. The documentation of threats should include the type of threat, identified threat groupings, motivations if any, and methods of actions.

In order to gain understanding of pertinent threats for the enterprise, researching past events may be helpful. Historically, there have been challenges to getting realistic breach data, but better reporting of post-breach findings continues to reduce the uncertainty of analysis. Another method to getting data is leveraging existing security technologies implemented to build a realistic perspective of threats.

The following are a few sample questions to guide you on the discovery of threats:

  • What is being detected by the existing infrastructure?

  • What are others in the same industry observing?

  • What post-breach data is available in the same industry vertical?

  • Who would want access to this data?

  • What would motivate a person to attempt unauthorized access to the data?

    • Data theft

    • Destruction

    • Notoriety

    • Hacktivism

    • Retaliation

A sample table of data type, threat, and motivation is shown as follows:

Data

 

Threat

 

Motivation

 

Credit card numbers

 

Hacker

 

Theft, Cybercrime

 

Trade secrets

 

Competitor

 

Competitive advantage

 

Personally Identifiable Information (PII)

 

Disgruntled employee

 

Retaliation, Destruction

 

Company confidential documents

 

Accidental leak

 

None

 

Client list

 

Natural disaster

 

None

 

This should be developed with as much detail as possible to form a realistic view of threats to the enterprise. There may also be several variations of threats and motivations for threat action on enterprise data. For example, accessing trade secrets by a competitor may be for competitive advantage, or a hacker may take action as part of hacktivism to bring negative press to the enterprise.

The more you can elaborate on the possible threats and motivations that exist, the better you will be able to reduce the list to probable threats based on challenging the data you have gathered. It is important to continually challenge the logic used to have the most realistic perspective.

Assessing impact

Now that the probable threats have been identified, what kind of damage can be done or negative impact can be enacted upon the enterprise and the data. Impact is the outcome of threats acting against the enterprise. This could be a denial-of-service state where the agent, a hacker, uses a tool to starve the enterprise Internet web servers of resources causing a denial-of-service state for legitimate users. Another impact could be the loss of customer credit cards resulting in online fraud, reputation loss, and countless dollars in cleanup and remediation efforts.

There are the immediate impacts and residual impacts. Immediate impacts are rather easy to determine because, typically, this is what we see in the news if it is big enough of an issue. Hopefully, the impact data does not come from first-hand experience, but in the case it is, executives should take action and learn from their mistakes. If there is no real-life experience with the impact, researching breach data will help using Internet sites such as DATALOSS db (http://datalossdb.org). Also, understanding the value of the data to the enterprise and its customers will aide in impact calculation. I think the latter impact analysis is more useful, but if the enterprise is unsure, then relying on breach data may be the only option.

The following are a few sample discovery questions for business impact analysis:

  • How is the enterprise affected by threat actions?

  • Will we go out of business?

  • Will we lose market share?

  • If the data is deleted or manipulated, can it be recovered or restored?

  • If the building is destroyed, do we have disaster recovery and business continuity capabilities?

To get a more accurate assessment of the probable impact or total cost to the enterprise, map out what data is most desirable to steal, destroy, and manipulate. Align the identified threats to the identified data, and apply an impact level to the data indicating if the enterprise would suffer critical to minor loss. These should be as accurate as possible. Work the scenarios out on paper and base the impact analysis on the outcome of the exercises.

The following is a sample table to present the identification and assessment of impact based on threat for a retailer. This is generally called a business impact analysis.

Data

 

Threat

 

Impact

 

Credit card numbers

 

Hacker

 

Critical

 

Trade secrets

 

Competitor

 

Medium

 

PII

 

Disgruntled employee

 

High

 

Company confidential documents

 

Accidental leak

 

Low

 

Client list

 

Natural disaster

 

Medium

 

Enterprise industry vertical may affect the impact analysis. For instance, a retailer may have greater impact if credit card numbers are stolen than if their client list was stolen. Both scenarios have impact but one may warrant greater protection and more restricted access to limit the scope of impact, and reduce immediate and residual loss.

Business impact should be measured in how the threat actions affect the business overall. Is it an annoyance or does it mean the business can no longer function? Natural disasters should also be accounted for and considered when assessing enterprise risk.

Assessing probability

Now that all conceived threats have been identified along with the business impact for each scenario, how do we really determine risk? Shouldn't risk be based on how likely the threat may take action, succeed, and cause an impact? Yes! The threat can be the most perilous thing imagined but if threat actions may only occur once in three thousand years, investment in protecting against the threat may not be warranted, at least in the near term.

Probability data is as difficult, if not more difficult, to find than threat data. However, this calculation has the most influence on the derived risk. If the identified impact is expected to happen twice a year and the business impact is critical, perhaps security budget should be allocated to security mechanisms that mitigate or reduce the impact. The risk of the latter scenario would be higher because it is more probable, not possible, but probable. Anything is possible. I have heard an analogy for this to make the point. In the game of Russian roulette, a semi-automatic pistol either has a bullet in the chamber or it does not, this is possible. With a revolver and a quick spin of the cylinder, you now have a 1 in 6 chance on whether there is a bullet that will be fired when the firing pin strikes forward. This is oversimplified to illustrate possibility versus probability. There are several variables in the example that could affect the outcome such as a misfire, or the safety catch being enabled, stopping the gun's ability to fire. These would be calculated to form an accurate risk value. Make sense?

This is how we need to approach probability. Technically, it is a semi-accurate estimation because there is just not enough detailed information on breaches and attacks to draw absolute conclusions. One approach may be to research what is happening in the same industry using online resources and peer groups, and then make intelligent estimates to determine if the enterprise could be affected too. Generally, there are outlier scenarios that require the utmost attention regardless; start here if these have not been identified as a probable risk scenario for the enterprise.

The following are a few sample probability estimation questions:

  • Has this event occurred before to the enterprise?

  • Is there data to suggest it is happening now?

  • Are there documented instances for similar enterprises?

  • Do we know anything in regards to occurrence?

  • Is the identified threat and impact really probable?

The following table is the continuation of our risk analysis for our fictional retailer:

Data

 

Threat

 

Impact

 

Probability

 

Credit card numbers

 

Hacker

 

Critical

 

High

 

Trade secrets

 

Competitor

 

Medium

 

Low

 

PII

 

Disgruntled employee

 

High

 

Medium

 

Company confidential documents

 

Accidental leak

Low

 

Low

Client list

 

Natural disaster

 

Medium

 

High

 

Based on the outcome of the probability exercises of identified threats and impacts, risk can be calculated and the appropriate course of action(s) developed and implemented.

Assessing risk

Now that the enterprise has agreed on what data has value, identified threats to the data, rated the impact to the enterprise, and the estimated probability of the impact occurring, the next logical step is to calculate the risk of the scenarios. Essentially, there are two methods to analyze and present risk: qualitative and quantitative. The decision to use one over the other should be based on the maturity of the enterprise's risk office. In general, a quantitative risk analysis will use descriptive labels like a qualitative method, however, there is more financial and mathematical analysis in quantitative analysis.

Qualitative risk analysis

Qualitative risk analysis provides a perspective of risk in levels with labels such as Critical, High, Medium, and Low. The enterprise must still define what each level means in a general financial perspective. For instance, a Low risk level may equate to a monetary loss of $1,000 to $100,000. The dollar ranges associated with each risk level will vary by enterprise. This must be agreed on by the entire enterprise so when risk is discussed, everyone is knowledgeable of what each label means financially. Do not confuse the estimated financial loss with the more detailed quantitative risk analysis approach; it is a simple valuation metric for deciding how much investment should be made based on probable monetary loss. The following section is an example qualitative risk analysis presenting the type of input required for the analysis. Notice that this is not a deep analysis of each of these inputs; it is designed to provide a relatively accurate perspective of risk associated with the scenario being analyzed.

Qualitative risk analysis exercise

Scenario: Hacker attacks website to steal credit card numbers located in backend database.

Threat: External hacker.

Threat capability: Novice to pro.

Threat capability logic: There are several script-kiddie level tools available to wage SQL injection attacks. SQL injection is also well documented and professional hackers can use advanced techniques in conjunction with the automated tools.

Vulnerability: 85 percent (how effective would the threat be with current mitigating mechanisms).

Estimated impact: High, Medium, Low (as indicated in the following table).

 

Risk

Estimated loss ($)

 

High

 

> 1,000,000

 

Medium

 

500,000 to 900,000

 

Low

 

< 500,000

 

Quantitative risk analysis

Quantitative risk analysis is an in-depth assessment of what the monetary loss would be to the enterprise if the identified risk were realized. In order to facilitate this analysis, the enterprise must have a good understanding of its processes to determine a relatively accurate dollar amount for items such as systems, data restoration services, and man-hour break down for recovery or remediation of an impacting event. Typically, enterprises with a mature risk office will undertake this type of analysis to drive priority budget items or find areas to increase insurance, effectively transferring business risk. This will also allow for accurate communication to the board and enterprise executives to know at any given time the amount of risk the enterprise has assumed.

With the quantitative approach a more accurate assessment of the threat types, threat capabilities, vulnerability, threat action frequency, and expected loss per threat action are required and must be as accurate as possible. As with qualitative risk analysis, the output of this analysis has to be compared to the cost to mitigate the identified threat. Ideally, the cost to mitigate would be less than the loss expectancy over a determined period of time. This is simple return on investment (ROI) calculation. Let's look again at the scenario used in the qualitative analysis and run it through a quantitative analysis. We will then compare against the price of a security product that would mitigate the risk to see if it is worth the capital expense.

Before we begin the quantitative risk analysis, there are a couple of terms that need to be explained:

  • Annual loss expectancy (ALE): The ALE is the calculation of what the financial loss would be to the enterprise if the threat event was to occur for a single year period. This is directly related to threat frequency. In the scenario this is once every three years, dividing the single lost expectancy by annual occurrence provides the ALE.

  • Cost of protection (COP): The COP is the capital expense associated with the purchase or implementation of a security mechanism to mitigate or reduce the risk scenario. An example would be a firewall that costs $150,000 and $50,000 per each year of protection of the loss expectancy period. If the cost of protection over the same period is lower than the loss, this is a good indication that the capital expense is financially worthwhile.

Quantitative risk analysis exercise

Scenario: Hacker attacks website to steal credit card numbers located in backend database.

Threat: External hacker.

Threat capability: Novice to pro.

Threat capability logic: There are several script-kiddie level tools available to wage SQL injection attacks. SQL injection is also well documented and professional hackers can use advanced techniques in conjunction with the automated tools.

Vulnerability: 85 percent (how effective would the threat be with current mitigating mechanisms).

Single loss expectation: $250,000.

Threat frequency: 3 (how many times per year; this would be roughly once every three years).

ALE: $83,000.

COP: $150,000 (over 3 years).

We will divide the total loss and the cost of protection over three years as, typically, capital expenses are depreciated over three to four years, and the loss is expected once every three years. This will give us the ALE and COP in the equation to determine the cost-benefit analysis.

This is a simplified example, but the math would look as follows:

$83,000 (ALE) - $50,000 (COP) = $33,000 (cost benefit)

The loss is annually $33,000 more than the cost to protect against the threat. The assumption in our example is that the $250,000 figure is 85% of the total asset value, but because we have 15% protection capability, the number is now approximately $294,000. This step can be shortcut out of the equation if the ALE and rate of occurrence are known.

When trying to figure out threat capability, try to be as realistic about the threat first. This will help us to better assess vulnerability because you will have a more accurate perspective on how realistic the threat is to the enterprise. For instance, if your scenario requires cracking advanced encryption and extensive system experience, the threat capability would be expert indicating current security controls may be acceptable for the majority of threat agents reducing probability and calculated risk. We tend to exaggerate in security to justify a purchase. We need to stop this trend and focus on what is the best area to spend precious budget dollars.

The ultimate goal of a quantitative risk analysis is to ensure that spend for protection does not far exceed the threat the enterprise is protecting against. This is beneficial for the security team in justifying the expense of security budget line items.

When the analysis is complete, there should still be a qualitative risk label associated with the risk. Using the above scenario with an annualized risk of $50,000 indicates this scenario is extremely low risk based on the defined risk levels in the qualitative risk exercise even if SLE is used. Does this analysis accurately represent acceptable loss? After an assessment is complete it is good practice to ensure all assumptions still hold true, especially the risk labels and associated monetary amounts.

Applying risk analysis to trust models

Well, now we can apply our risk methodology to our trust models to decide if we can continue with our implementation as is, or whether we need to change our approach based on risk. Our trust models, which are essentially use cases, rely on completing the risk analysis, which in turn decide the trust level and security mechanisms required to reduce the enterprise risk to an acceptable level.

It would be foolish to think that we can shove all requests for similar access directly into one of these buckets without further analysis to determine the real risk associated with the request. After completing one of the risk analysis types we just covered, risk guidance can be provided for the scenario (and I stress guidance). For the sake of simplicity an implementation path may be chosen, but it will lead to compromises in the overall security of the enterprise and is cautioned. I have re-presented the table of one scenario, the external application user. This is a better representation of how a trust model should look with risk and security enforcement established for the scenario. If an enterprise is aware of how it conducts business, then a focused effort in this area should produce a realistic list of interactions with data by whom, with what level of trust, and based on risk, what controls need to be present and enforced by policy and standards.

User type

 

External

 

Allowed access

 

Tier 1 DMZ only, Least privilege

 

Trust level

 

1 - Not trusted

 

Risk

 

Medium

 

Policy

 

Acceptable use, Monitoring, Access restrictions

 

Required security mechanisms

 

FW, IPS, Web application firewall

 

The user is assumed to have access to log in to the web application and have more possible interaction with the backend database(s). This should be a focal point for testing, because this is the biggest area of risk in this scenario. Threats such as SQL injection that can be waged against a web application with little to no experience are commonplace. Enterprises that have e-commerce websites typically do not restrict who can create an account. This should have input to the trust decision and ultimately the security architecture applied.

Deciding on a risk analysis methodology

We have covered the two general types of risk analysis, qualitative and quantitative, but which is best? It depends on several factors: risk awareness of the enterprise, risk analysts' capabilities, risk analysis data, and the influence of risk in the enterprise. If the idea of risk analysis or IT risk analysis is new to the enterprise, then a slow approach with qualitative analysis is recommended to get everyone thinking of risk and what it means to the business. It will be imperative to get an enterprise-wide agreement on the risk labels. Using the lesser involved method does not mean you will not be questioned on the data used in the analysis, so be prepared to defend the data used and explain estimation methods leveraged.

If it is decided to use a quantitative risk analysis method, a considerable amount of effort is required along with meticulous loss figures and knowledge of the environment. This method is considered the most effective requiring risk expertise, resources, and an enterprise-wide commitment to risk analysis. This method is more accurate, though it can be argued that since both methods require some level of estimation, the accuracy lies in accurate estimation skills.

I use the Douglas Hubbard school of thought on estimating with 90 percent accuracy. You will find his works at his website http://www.hubbardresearch.com/. I highly recommend his title How to Measure Anything: Finding the Value of "Intangibles" in Business, Tantor Media to learn estimation skills. It may be beneficial to have an external firm perform the analysis if the engagement is significant in size.

The benefits of both should be that the enterprise is able to make risk-aware decisions on how to securely implement IT solutions. Both should be presented with common risk levels such as High, Medium, Low; essentially the common language everyone can speak knowing a range of financial risk without all the intimate details of how they arrived at the risk level.

Other thoughts on risk and new enterprise endeavors

Now that you have been presented with types of risk analysis, they should be applied as tools to best approach the new technologies being implemented in the networks of our enterprises. Unfortunately, there are broad brush strokes of trusted and untrusted approaches being applied that may or may not be accurate without risk analysis as a decision input. Two examples where this can be very costly are the new BYOD and cloud initiatives. At first glance these are the two most risky business maneuvers an enterprise can attempt from an information security perspective. Deciding if this really is the case requires an analysis based on trust models and data-centric security architecture. If the proper security mechanisms are implemented and security applied from users to data, the risk can be reduced to a tolerable level. The BYOD business model has many positive benefits to the enterprise, especially capital expense reduction. However, implementing a BYOD or cloud solution without further analysis of risk can introduce significant risk beyond the benefit of the initiative.

Do not be quick to spread fear in order to avoid facing the changing landscape we have worked so hard to build and secure. It is different, but at one time, what we know today as the norm was new too. Be cautious but creative, or IT security will be discredited for what will be received as a difficult interaction. This is not the desired perception for IT security. Strive to understand the business case, risk to business assets (data, systems, people, processes, and so on), and then apply sound security architecture as we have discussed so far. Begin evangelizing the new approach to security in the enterprise by developing trust models that everyone can understand. Use this as the introduction to agile security architecture and get input to create models based on risk. By providing a risk-based perspective to emerging technologies and other radical requests, a methodical approach can bring better adoption and overall increased security in the enterprise.

Summary

In this article, we took a look at analyzing risk by presenting quantitative and qualitative methods including an exercise to understand the approach. The overall goal of security is to be integrated into business processes, so it is truly a part of the business and not an expensive afterthought simply there to patch a security problem.

Resources for Article :


Further resources on this subject:


Enterprise Security: A Data-Centric Approach to Securing the Enterprise A guide to applying data-centric security concepts for securing enterprise data to enable an agile enterprise with this book and ebook.
Published: February 2013
eBook Price: $26.99
Book Price: $44.99
See more
Select your format and quantity:

About the Author :


Aaron Woody

Aaron Woody is an expert in information security with over 15 years of experience across several industry verticals. His experience includes securing some of the largest enterprises in the world. Currently, he is a Security Consultant in the public sector. He is also a speaker and active instructor teaching hacking and forensics, and maintains a blog n00bpentesting.com.

Aaron can also be followed on twitter at @shai_saint. He will be launching a companion website (http://www.datacentricsec.com) for this book. To contact him for consulting please e-mail him at aaron.m.woody@gmail.com.

Books From Packt


 Open Text Metastorm ProVision® 6.2 Strategy Implementation
Open Text Metastorm ProVision® 6.2 Strategy Implementation

 BlackBerry Enterprise Server for Microsoft® Exchange
BlackBerry Enterprise Server for Microsoft® Exchange

 eZ Publish 4: Enterprise Web Sites Step-by-Step
eZ Publish 4: Enterprise Web Sites Step-by-Step

 Microsoft Silverlight 5 and Windows Azure Enterprise Integration
Microsoft Silverlight 5 and Windows Azure Enterprise Integration

 Securing WebLogic Server 12c
Securing WebLogic Server 12c

 Microsoft Enterprise Library 5.0
Microsoft Enterprise Library 5.0

 Troux Enterprise Architecture Solutions
Troux Enterprise Architecture Solutions

 MVVM Survival Guide for Enterprise Architectures in Silverlight and WPF
MVVM Survival Guide for Enterprise Architectures in Silverlight and WPF


No votes yet

Post new comment

CAPTCHA
This question is for testing whether you are a human visitor and to prevent automated spam submissions.
u
F
y
v
H
n
Enter the code without spaces and pay attention to upper/lower case.
Code Download and Errata
Packt Anytime, Anywhere
Register Books
Print Upgrades
eBook Downloads
Video Support
Contact Us
Awards Voting Nominations Previous Winners
Judges Open Source CMS Hall Of Fame CMS Most Promising Open Source Project Open Source E-Commerce Applications Open Source JavaScript Library Open Source Graphics Software
Resources
Open Source CMS Hall Of Fame CMS Most Promising Open Source Project Open Source E-Commerce Applications Open Source JavaScript Library Open Source Graphics Software