Analytic Reinforcements

Rumbi’s post of last week on ransomware got me thinking on a long drive back from Washington about what an excellent tool the AICPA’s new Cybersecurity Risk Management Reporting Framework is, not only for CPAs but for CFEs as well as for all our client organizations. As the seemingly relentless wave of cyberattacks continues with no sign of let up, organizations are under intense pressure from key stakeholders and regulators to implement and enhance their cyber security and fraud prevention programs to protect customers, employees and all the types of valuable information in their possession.

According to research from the ACFE, the average total cost per company, per event of a data breach is $3.62 million. Initial damage estimates of a single breach, while often staggering, may not take into account less obvious and often undetectable threats such as the theft of intellectual property, espionage, destruction of data, attacks on core operations or attempts to disable critical infrastructure. These effects can knock on for years and have devastating financial, operational and brand impact ramifications.

Given the present broad regulatory pressures to tighten cyber security controls and the visibility surrounding cyberrisk, a number of proposed regulations focused on improving cyber security risk management programs have been introduced in the United States over the past few years by our various governing bodies. One of the more prominent is a regulation by the New York Department of Financial Services (NYDFS) that prescribes certain minimum cyber security standards for those entities regulated by the NYDFS. Based on an entity’s risk assessment, the NYDFS law has specific requirements around data encryption and including data protection and retention, third-party information security, application security, incident response and breach notification, board reporting, and required annual re-certifications.

However, organizations continue to report to the ACFE regarding their struggle to systematically report to stakeholders on the overall effectiveness of their cyber security risk management programs. In response, the AICPA in April of last year released a new cyber security risk management reporting framework intended to help organizations expand cyberrisk reporting to a broad range of internal and external users, to include management and the board of directors. The AICPA’s new reporting framework is designed to address the need for greater stakeholder transparency by providing in-depth, easily consumable information about the state of an organization’s cyberrisk management program. The cyber security risk management examination uses an independent, objective reporting approach and employs broader and more flexible criteria. For example, it allows for the selection and utilization of any control framework considered suitable and available in establishing the entity’s basic cyber security objectives and in developing and maintaining controls within the entity’s cyber security risk management program irregardless of whether the standard is the US National Institute of Standards and Technology (NIST)’s Cybersecurity Framework, the International Organization for standardization (ISO)’s ISO 27001/2 and related frameworks, or even an internally developed framework based on a combination of sources. The examination is voluntary, and applies to all types of entities, but should be considered by CFEs as a leading practice that provides management, boards and other key stakeholders with clear insight into the current state of an organization’s cyber security program while identifying gaps or pitfalls that leave organizations vulnerable to cyber fraud and other intrusions.

What stakeholders might benefit from a client organization’s cyber security risk management examination report? Clearly, we CFEs as we go about our routine fraud risk assessments; but such a report, most importantly, can be vital in helping an organization’s board of directors establish appropriate oversight of a company’s cyber security risk program and credibly communicate its effectiveness to stakeholders, including investors, analysts, customers, business partners and regulators. By leveraging this information, boards can challenge management’s assertions around the effectiveness of their cyberrisk management and fraud prevention programs and drive more effective decision making. Active involvement and oversight from the board can help ensure that an organization is paying adequate attention to cyberrisk management and displaying due diligence. The board can help shape expectations for reporting on cyberthreats while also advocating for greater transparency and assurance around the effectiveness of the program.

The cyber security risk management report in its initial and follow-up iterations can be invaluable in providing overview guidance to CFEs and forensic accountants in targeting both fraud prevention and fraud detection/investigative analytics. We know from our ACFE training that data analytics need to be fully integrated into the investigative process. Ensuring that data analytics are embedded in the detection/investigative process requires support from all levels, starting with the managing CFE. It will be an easier, more coherent process for management to support such a process if management is already supporting cyber security risk management reporting. Management will also have an easier time reinforcing the use of analytics generally, although the data analytics function supporting fraud examination will still have to market its services, team leaders will still be challenged by management, and team members will still have to be trained to effectively employ the newer analytical tools.

The presence of a robust cyber security risk management reporting process should also prove of assistance to the lead CFE in establishing goals for the implementation and use of data analytics in every investigation, and these goals should be communicated to the entire investigative team. It should be made clear to every level of the client organization that data analytics will support the investigative planning process for every detected fraud. The identification of business processes, IT systems, data sources, and potential analytic routines should be discussed and considered not only during planning, but also throughout every stage of the entire investigative engagement. Key in obtaining the buy-in of all is to include investigative team members in identifying areas or tests that the analytics group will target in support of the field work. Initially, it will be important to highlight success stories and educate managers and team leaders about what is possible. Improving on the traditional investigative approach of document review, interviewing, transaction review, etc. investigators can benefit from the implementation of data analytics to allow for more precise identification of the control deficiencies, instances of noncompliance with policies and procedures, and mis-assessment of areas of high risk that contributed to the development of the fraud in the first place. These same analytics can then be used to ensure that appropriate post-fraud management follow-up has occurred by elevating the identified deficiencies to the cyber security risk management reporting process and by implementing enhanced fraud prevention procedures in areas of higher fraud risk. This process would be especially useful in responding to and following up data breaches.

Once patterns are gathered and centralized, analytics can be employed to measure the frequency of occurrence, the bit sizes, the quantity of files executed and average time of use. The math involved allows an examiner to grasp the big picture. Individuals, including examiners, are normally overwhelmed by the sheer volume of information, but automation of pattern recognizing techniques makes big data a tractable investigative resource. The larger the sample size, the easier it is to determine patterns of normal and abnormal behavior. Network haystacks are bombarded by algorithms that can notify the CFE information archeologist about the probes of an insider threat for example.

Without analytics, enterprise-level fraud examination and risk assessment is a diminished discipline, limited in scope and effectiveness. Without an educated investigative workforce, armed with a programing language for automation and an accompanying data-mining philosophy and skill set, the control needs of management leaders at the enterprise level will go unmet; leaders will not have the data needed for fraud prevention on a large scale nor a workforce that is capable of getting them that data in the emergency following a breach or penetration.

The beauty of analytics, from a security and fraud prevention perspective, is that it allows the investigative efforts of the CFE to align with the critical functions of corporate business. It can be used to discover recurring risks, incidents and common trends that might otherwise have been missed. Establishing numerical baselines on quantified data can supplement a normal investigator’s tasks and enhance the auditor’s ability to see beneath the surface of what is presented in an examination. Good communication of analyzed data gives decision makers a better view of their systems through a holistic approach, which can aid in the creation of enterprise-level goals. Analytics and data mining always add dimension and depth to the CFE’s examination process at the enterprise level and dovetail with and are supported beautifully by the AICPA’s cyber security risk management reporting initiative.

CFEs should encourage the staffs of client analytics support functions to possess …

–understanding of the employing enterprise’s data concepts (data elements, record types, database types, and data file formats).
–understanding of logical and physical database structures.
–the ability to communicate effectively with IT and related functions to achieve efficient data acquisition and analysis.
–the ability to perform ad hoc data analysis as required to meet specific fraud examiner and fraud prevention objectives.
–the ability to design, build, and maintain well-documented, ongoing automated data analysis routines.
–the ability to provide consultative assistance to others who are involved in the application of analytics.

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