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This chapter is from the book

Traditional Risk Analysis Terminology

An in-depth analysis of all existing risk analysis approaches is beyond the scope of this book; instead, I summarize basic approaches, common features, strengths, weaknesses, and relative advantages and disadvantages.

As a corpus, “traditional” methodologies are varied and view risk from different perspectives. Examples of basic approaches include the following:

  • Financial loss methodologies that seek to provide a loss figure to be balanced against the cost of implementing various controls
  • Mathematically derived “risk ratings” that equate risk to arbitrary ratings for threat, probability, and impact
  • Qualitative assessment techniques that base risk assessment on anecdotal or knowledge-driven factors

Each basic approach has its merits, but even when approaches differ in the details, almost all of them share some common concepts that are valuable and should be considered in any risk analysis. These commonalities can be captured in a set of basic definitions.

  • Asset: The object of protection efforts. This may be variously defined as a system component, data, or even a complete system.

  • Risk: The probability that an asset will suffer an event of a given negative impact. Various factors determine this calculation: the ease of executing an attack, the motivation and resources of an attacker, the existence of vulnerabilities in a system, and the cost or impact in a particular business context. Risk = probability × impact.

  • Threat: The actor or agent who is the source of danger. Within information security, this is invariably the danger posed by a malicious agent (e.g., fraudster, attacker, malicious hacker) for a variety of motivations (e.g., financial gain, prestige). Threats carry out attacks on the security of the system (e.g., SQL injection, TCP/IP SYN attacks, buffer overflows, denial of service). Unfortunately, Microsoft has been misusing the term threat as a substitute for risk. This has led to some confusion in the commercial security space. (See the next box, On Threat Modeling versus Risk Analysis: Microsoft Redefines Terms.)

  • Vulnerability: For a threat to be effective, it must act against a vulnerability in the system. In general, a vulnerability is a defect or weakness in system security procedures, design, implementation, or internal controls that can be exercised and result in a security breach or a violation of security policy. A vulnerability may exist in one or more of the components making up a system. (Note that the components in question are not necessarily involved with security functionality.) Vulnerability data for a given software system are most often compiled from a combination of OS-level and application-level vulnerability test results (often automated by a “scanner,” such as Nessus, Nikto, or Sanctum’s Appscan), code reviews, and higher-level architectural reviews. In software, vulnerabilities stem from defects and come in two basic flavors: flaws are design-level problems leading to security risk, and bugs are implementation-level problems leading to security risk. Automated source code analysis tools tend to focus on bugs. Human expertise is required to uncover flaws.

  • Countermeasures or safeguards: The management, operational, and technical controls prescribed for an information system which, taken together, adequately protect the confidentiality, integrity, and availability of the system and its information. For every risk, controls may be put in place that either prevent or (at a minimum) detect the risk when it triggers.

  • Impact: The impact on the organization using the software, were the risk to be realized. This can be monetary or tied to reputation, or may result from the breach of a law, regulation, or contract. Without a quantification of impact, technical vulnerability is hard to deal with—especially when it comes to mitigation activities. (See the discussion of the “techno-gibberish problem” in Chapter 2.)

  • Probability: The likelihood that a given event will be triggered. This quantity is often expressed as a percentile, though in most cases calculation of probability is extremely rough. I like to use three simple buckets: high (H), medium (M), and low (L). Geeks have an unnatural propensity to use numbers even when they’re not all that useful. Watch out for that when it comes to probability and risk. Some organizations have five, seven, or even ten risk categories (instead of three). Others use exact thresholds (70%) and pretend-precision numbers, such as 68.5%, and end up arguing about decimals. Simple categories and buckets seem to work best, and they emerge from the soup of risks almost automatically anyway.

Using these basic definitions, risk analysis approaches diverge on how to arrive at particular values for these attributes. A number of methods calculate a nominal value for an information asset and attempt to determine risk as a function of loss and event probability. Some methods use checklists of risk categories, threats, and attacks to ascertain risk.

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