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“Intelligent Automation” is such a new term that you won’t find it in Wikipedia or Merriam-Webster. However, we are clearly in the early stages of a technological transformation that’s no less dramatic than the one spurred by the emergence of the Internet.
A new age in quantitative and empirical methods will change how businesses operate as well as the role of traditional finance professionals. To compete in this environment, finance teams must be willing to adopt new operating models that reduce costs and improve performance through better data. In short, a new framework is needed for designing an “intelligent organization.”
The convergence of technology and cognitive science provides finance professionals with powerful new tools to tackle complex problems with more certainty. Advanced analytics and automation will increasingly play bigger roles as tactical solutions to drive efficiency or to help executives solve complex problems.
But the real opportunities lie in reimaging the enterprise as intelligent organization — one designed to create situational awareness with tools capable of analyzing disparate data in real or near-real time.
Automation of redundant processes is only the first step. An intelligent organization strategically designs automation to connect disparate systems (e.g., data sources) by enabling users with tools to quickly respond or adjust to threats and opportunities in the business.
Situational awareness is the product of this design. In order to push decision-making deeper into the organization, line staff need the tools and information to respond to change in the business and the flexibility to adjust and mitigate problems within prescribed limits. Likewise, senior executives need near-real time data that provides the means to query performance across different lines of business with confidence and anticipate impacts to singular or enterprise events in order to avoid costly mistakes.
Financial reporting is becoming increasingly complex at the same time finance professionals are being challenged to manage emerging risks, reduce costs, and add value to strategic objectives. These competing mandates require new support tools that deliver intelligence and inspire greater confidence in the numbers.
Thankfully, a range of new automation tools is now available to help finance professionals achieve better outcomes against this dual mandate. However, to be successful finance executives need a new cognitive framework that anticipates the needs of staff and provides access to the right data in a resilient manner.
This cognitive framework provides finance with a design road map that includes human elements focused on how staff uses technology and simplifying the rollout and implementation of advanced analytical tools.
The framework is composed of five pillars, each designed to complement the others in the implementation of intelligent automation and the development of an intelligent organization:
- Cognitive governance
- Intentional control design
- Business intelligence
- Performance management
- Situational awareness
Cognitive governance is the driver of intelligent automation as a strategic tool in guiding organizational outcomes. The goal of cognitive governance, as the name implies, is to facilitate the design of intelligent automation to create actionable business intelligence, improve decision-making, and reduce manual processes that lead to poor or uncertain outcomes.
In other words, cognitive governance systematically identifies “blind spots” across the firm then directs intelligent automation to reduce or eliminate the blind spots.
The end game is to create situational awareness at multiple levels of the organization with better tools to understand risks, errors in judgment, and inefficient processes. Human error as a result of decision-making under uncertainty is increasingly recognized as the greatest risk to organizational success. Therefore, it is crucial for senior management create a systemic framework for reducing blind spots in a timely manner. Cognitive governance sets the tone and direction for the other four pillars.
Intentional control design, business intelligence, and performance management are tools for creating situational awareness in response to cognitive governance mandates. A cognitive framework does not require huge investments in the latest big data “shiny objects.” It’s not necessary to spend millions on machine learning or other forms of artificial intelligence. Alternative automation tools for simplifying operations are readily available today, as is access to advanced analytics, for organizations large and small, from a variety of cloud services.
However, for firms that want to use machine learning/AI, a cognitive framework easily integrates any widely used tool or regulatory risk framework. A cognitive framework is focused on a factor that others ignore: how humans interact with and use technology to get their work done most effectively.
Network complexity has been identified as a strategic bottleneck in response times for dealing with cybersecurity risks, cost of technology, and inflexibility in fast-paced business environments. Without a proper framework, improperly designed automation processes may simply add to infrastructure complexity.
There is also a dark side to machine learning/AI that organizations must understand in order to anticipate best use cases and avoid the inevitable missteps that will come with autonomous systems. Microsoft learned a hard lesson with “Clippy,” its Chatbot project, which was shelved when users taught the bot racist remarks. While there are many uses for AI, this technology is still in an experimental stage of growth.
Overly complicated approaches to intelligent automation are the leading cause of failed big data projects. Simplicity is the new value proposition that should be expected from the implementation of technology solutions. Intelligent automation is one tool to accomplish that goal, but execution requires a framework that understands how people use new technology effectively.
Simplicity must be a strategic design imperative based on a framework for creating situational awareness across the enterprise.
James Bone is a cognitive risk consultant; a lecturer at Columbia University’s School of Professional Studies; founder of TheGRCBlueBook.com, an online directory of governance, risk, and compliance tools; and author of, “Cognitive Hack: The New Battleground in Cybersecurity … the Human Mind.”
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Our [KPMG] work as audit professionals is fundamentally about “trust.” For the capital markets to operate effectively and to the benefit of investors and society more broadly, there must be integrity and confidence in the system. In serving the capital markets and the public interest, we work to help instill trust and confidence in the information used to make important decisions.
In the following pages, we begin to explore how we can continue to promote trust during a time of profound change across the business landscape. Given the explosion of data and the digitization of our lives, we want to promote a discussion about how the audit profession must evolve its tools and approach to keep up with the pace of change and remain relevant in a dynamic marketplace. Specifically, our profession must embrace the use of advanced technologies, including data and analytics (D&A), robotics, automation and cognitive intelligence, to manage processes, support planning and inform decision making. At KPMG we are constantly thinking about the development of innovative capabilities and technologies that will enhance quality and strengthen the relevance of our audit into the future.
In part I of Cognitive Risk Framework for Cybersecurity, I introduced the reasoning for developing a bridge from existing IT and risk frameworks to the next generation of risk management based on cognitive. These concepts are no longer theoretical and, in fact, are evolving faster than most IT security and risk professionals appreciate. In part II, I introduce the pillars of a cognitive risk framework for cybersecurity that make this program operational. The pillars represent existing technology and concepts that are increasingly being adopted by technology firms, government agencies, computer scientists and industries as diverse as healthcare, biotechnology, financial services and many others.
The following is an abbreviated version of the cognitive risk framework for cybersecurity that will be published later this year.
A cognitive risk framework is fundamental to the integration of existing internal controls, risk management practice, cognitive security technology and the people who are responsible for executing on the program components that make up enterprise risk management. Cognitive risk fills the missing gap in today cybersecurity program that fails to fully incorporate how to address the “softest target”, the human mind.
A functioning cognitive risk framework for cybersecurity provides guidance for the development of a CogSec response that is three-dimensional instead of a one-dimensional defensive posture. Further, cognitive risk requires an expanded taxonomy to level set expectations about risk management through scientific methods and improve communications about risks. A CRFC is an evolutionary step from intuition and hunches to quantitative analysis and measurement of risks. The first step in the transition to a CRFC is to develop an organizational Cognitive Map. Paul Slovic’s Perception of Risk research is a guide for starting the process to understand how decision-makers across an organization perceive key risks in order to prioritize actionable steps for a range of events large and small. A Cognitive Map is one of many tools risk professionals must use to expand discussions on risk and form agreements for enhanced techniques in cybersecurity.
Risk communications sound very simple on the surface but even risk experts will refer to risks and use the term with different meanings without recognizing the contradictions. In speaking with senior executives at a major bank I was told that she thought the understood risks but the 2008 Great Recession revealed major disagreements in how the firm talked about risk and the decisions made to manage risk. Poor communications about risk are more common than not without a structured way to put risks in context to account for a diversity of risk perceptions. “The fact that the word “risk” has so many different meanings often causes problems in communication, ” according to Slovic.
Organizations rarely openly discuss these differences or even understand they exist until a major risk event forces these issues onto the table. Even then the focus of the discussion quickly pivots to solving the problem with short-term solutions leaving the underlying conflicts unresolved. Slovic, Peters, Finucane and MacGregor (2005) posited that “risk is perceived and acted on it two ways: Risk as Feelings refers to individuals’ fast, instinctive, and intuitive reactions to danger. Risk as Analysis brings logic, reason, and scientific deliberation to bear on risk management.”
Some refer to this exercise as forming a “risk appetite” but again this term is vague and doesn’t fully develop a full range of ways individuals experience risk. Researchers now recognize diverse views of risks as relevant from the nonscientist who views risks subjectively to scientists who evaluate adverse events as the probability and consequences of risks. A deeper view into risk perceptions explains why there is little consensus on the role of risk management and dissatisfaction when expectations are not met.
Techniques for reconciling these differences create a forum that leads to better discussions about risk. Discussions about risk management are extremely important to organizational success yet paradoxically produce discomfort whether in personal or business life when planning for the future. Personal experience in conjunction with a body of research demonstrates that the topic of risk tends to elicit a strong emotional response. Kahneman and Tversky called this response “loss aversion”. “Numerous studies have shown that people feel losses more deeply than gains of the same value (Kahneman and Tversky 1979, Tversky and Kahneman 1991).” Losses have a powerful psychological impact that lingers long after the fact coloring one’s perception about risk taking.
Over time these perceptions about risk and loss become embedded in the unconscious and by virtue of the vagaries of memory the facts and circumstances fade. The natural bias to avoid loss leads us to a fallacy that assumes losses are avoidable if people simply make the right choices. This common view of risk awareness fails to account for uncertainty, the leading cause of surprise, when expectations are not met. This fallacy of perceived risks produces an underestimation or overestimation of the probability of success or failure.
A Cognitive Risk Framework for Cybersecurity, or any other risk, requires a clear understanding and agreement on the role(s) of data management; risk and decision support analytics, parameters for dealing with uncertainty (imperfect information), and how technology is integrated to facilitate the expansion of what Herbert A. Simon called “bounded rationality”. Building a CRFC does not eliminate risks it develops a new kind of intelligence about risk.
The goal of a cognitive risk framework is needed to advance risk management in the same way economists deconstructed the “rational man” theory. The myth of “homo economicus” still lingers in risk management damaging the credibility of the profession. “Homo economicus, economic man, is a concept in many economic theories portraying humans as consistently rational and narrowly self-interested who usually pursue their subjectively defined ends optimally”.[i] These concepts have since been contrasted with Simon’s bounded rationality; not to mention any number of financial market failures and unethical and fraudulent behavior that stands as evidence to the weakness in the argument. A cognitive risk framework will serve to broaden awareness in the science of cognitive hacks as well as the factors that limit our ability to effectively deal with the Cyber Paradox that go beyond selecting defensive strategy. Let’s take a closer look at what a cognitive risk framework for cybersecurity looks like and consider how to operationalize the program.
The foundational base (“Guiding Principles”) for developing a cognitive risk framework for cybersecurity starts with Slovic’s “Cognitive Map – Perceptions of Risk” and an orientation in Simon’s “Bounded Rationality” and Kahneman and Tversky’s “Prospect Theory – An Analysis of Decision Making Under Risk”. In other words, a cognitive risk framework formally develops a structure for actualizing the two ways people fundamentally perceive adverse events; “risk as feelings” and “risk as analysis”. Each of the following guiding principles is a foundational building block for a more rigorous science-based approach to risk management.
The CRFC guiding principles expand the language of risk with concepts from behavioral science to build a bridge connecting decision science, technology and risk management. The CRFC guiding principles establish a link and recognize the important work undertaken by the COSO Enterprise Risk Framework for Internal Controls, ISO 31000 Risk Management Framework, NIST and ISO/IEC 27001 Information Security standards; which make reference to the need for processes to deal with the human element. The opportunity to extend the cognitive risk framework to other risk programs exists however the focus of this topic is directed on cybersecurity and the program components needed to operationalize its execution. The CRFC program components include five pillars: 1) Intentional Controls Design; 2) Cognitive Informatics Security (Security Informatics); 3) Cognitive Risk Governance; 4) Cybersecurity Intelligence & Active Defense Strategies; and, 5) Legal “Best Efforts” Considerations in Cyberspace.
Brief overview of the Five Pillars of a CRFC:
Intentional Controls Design
Intentional controls design recognizes the importance of trust in networked information systems by advocating for the automation of internal controls design integration for IT, operational, audit and compliance controls. Intentional controls design is the process of embedding information security controls, active monitoring, audit reporting, risk management assessment and operational policy and procedure controls into network information systems through user-guided GUI application design and data repository to enable machine learning, artificial intelligence and other currently available smart system methods.
Intentional controls design is an explicit choice made by information security analysts to reduce or remove reliance on people through the use of automated controls. Automated controls must be animated through the used of machine learning, artificial intelligence algorithms, and other automation based on regulatory guidance and internal policy. Intentional controls design is implemented on two levels of hierarchy: 1) Enterprise level intentional controls design anticipates that these controls are mandatory across the organization and can only be changed or modified by senior executive approval responsible for enterprise governance; 2) Operational level intentional controls design anticipates that each division or business unit may require unique control design to account for lines of business difference in regulatory regimes, risk profile, vendor relationships and other unique to these operations.
Cognitive Informatics Security (Security Informatics)
Cognitive informatics security is a rapidly evolving discipline within cybersecurity and healthcare with many branches of discipline making it difficult to come up with one definition. Think of cognitive security as an overarching strategy for cybersecurity executed through a variety of advanced computing methodologies.
“Cognitive computing has the ability to tap into and make sense of security data that has previously been dark to an organization’s defenses, enabling security analysts to gain new insights and respond to threats with greater confidence at scale and speed. Cognitive systems are taught, not programmed, using the same types of unstructured information that security analysts rely on.”[i]
The International Journal of Cognitive Informatics and Natural Intelligence defines cognitive informatics as, “ a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain.”[ii]
Cyber Risk Governance
The Cyber Risk Governance pillar is concerned with the role of the Board of Directors and senior management in strategic planning and executive sponsorship of cybersecurity. Boards of director historically delegate risk and compliance reporting to the Audit Committee although a few forward thinking firms have appointed a senior risk executive who reports directly to the BoD. In order to implement a Cognitive Risk Framework for Cybersecurity the entire board must participate in an orientation of the guiding principles to set the stage and tone for the transformation required to incorporate cognition into a security program.
The framework represents a transformational change in risk management, cybersecurity defense and an understanding of decision-making under uncertainty. To date, traditional risk management has lacked scientific rigor through quantitative analysis and predictive science. The framework dispels myths about risk management while aligning the practice of security and risk management using the best science and technology available today and the future.
Transformational change from an old to a new framework requires leadership from the board and senior management that goes beyond the sponsorship of a few new initiatives. The framework represents a fundamentally new vision for what is possible in risk and security to address cybersecurity or enterprise risk management. Change is challenging for most organizations however the transformation required to move to a new level of cognition may be the hardest, but most effective, any firm will ever undertake. This is exactly why the board and senior management must understand the framing of decision-making and the psychology of choice. Why, you may ask, must senior management understand what one does naturally and intuitively? The answer is that change is a choice and the process of decision-making among a set of options is not as intuitive or simple as one thinks.
Cybersecurity Intelligence and Defense Strategies
“Information on its own maybe of utility to the commander, but when related to other information about the operational environment and considered in the light of past experience, it gives rise to a new understanding of the information, which may be termed “intelligence.”[i]
The Cybersecurity Intelligence and Defense Strategies (CIDS) pillar is based on the principles of the 17-member Defense Intelligence and Intelligence community “Joint Intelligence” report. Cybersecurity intelligence is conducted to develop information on four levels – Strategic, Operational, Tactical & Asymmetrical. Strategic intelligence should be developed for the board of directors, senior management and the Cyber Risk Governance committee. Operational intelligence should be designed to provide security professionals with an understanding of threats and operational environment vulnerabilities. Tactical intelligence must provide directional guidance for offensive and defensive security strategies. Asymmetrical intelligence strategies include monitoring the cyber black market and other market intelligence from law enforcement and other means as possible.
CIDS also acts as the laboratory for cybersecurity intelligence responsible for leading the human and technology security practice through a data dependent format to provide rapid response capabilities. Information gathering is the process of providing organizational leadership with context for improved decision-making for current and forward-looking objectives that are key to operational success or to avoid operational failure. Converting information into intelligence requires an organization to develop formal processes, capabilities, analysis, monitoring, and communication channels that enhance its ability to respond appropriately and in a timely manner. Intelligence gathering assumes that the organization has in place objectives for cybersecurity that are well defined through plans of execution and possesses capabilities to respond accordingly to countermeasures (surprise) as well as expected outcomes.
Legal “Best Efforts” Considerations in Cyberspace
To say that the legal community is struggling with how to address cyberrisks is an understatement on the one hand addressing the protection of their own client’s data and on the other hand determining negligence in an global environment where no organization can ensure against a data breach with 100% certainty. “The ABA Cybersecurity Legal Task Force, chaired by Judy Miller and Harvey Rishikof, is hard at work on the Cyber and Data Security Handbook. The Cyber Incident Response Handbook, which originated with the Task Force.”[i] Law firms have the same challenges as all other organizations but also have a higher standard in their ethical rules that require confidentiality of attorney-client and work product data. I looked to the guidance provided by the ABA to frame the fifth pillar of the CRFC.
The concept of “best efforts” is a contractual term used to obligate the parties to make their best attempt to accomplish a goal, typically used when there is uncertainty about the ability to meet a goal. “Courts have not required that a party under a duty to use best efforts to accomplish a given goal make every available effort to do so, regardless of the harm to it. Some courts have held that the appropriate standard is one of good faith. Black’s Law Dictionary 701 (7th ed. 1999) has defined good faith as “A state of mind consisting in (1) honesty in belief or purpose, (2) faithfulness to one’s duty or obligation, (3) observance of reasonable commercial standards of fair dealing in a given trade or business, or (4) absence of intent to defraud or to seek unconscionable advantage””.[ii]
Boards of director and senior executives are held to these standards by contractual agreement whether aware of these standards or not in the event a breach occurs. The ABA has adopted a security program guide by the Carnegie Mellon University’s Software Engineering Institute. The Carnegie Mellon Enterprise Security Program (ESP) has been tailored for law firms as a prescriptive set of security related activities as well as incident response and ethical considerations. The Carnegie Mellow ESP spells out “some basic activities must be undertaken to establish a security program, no matter which best practice a firm decides to follow. (Note that they are all harmonized and can be adjusted for small firms.) Technical staff will manage most of these activities, but firm partners and staff need to provide critical input. Firm management must define security roles and responsibilities, develop top-level policies and exercise oversight. This means reviewing findings from critical activities; receiving regular reports on intrusions, system usage and compliance with policies and procedures; and reviewing the security plans and budget.”
This is information is not legal guidance to comply with an organization’s best efforts requirements. The information is provided to bring awareness to the importance the board and senior management’s participation to ensure all bases are covered in cyberrisk. The CRFC’s fifth pillar completes the framework as a link to existing standards of information security with an enhanced approach that includes cognitive science.
A cognitive risk framework for cybersecurity represents an opportunity to accelerate advances in cybersecurity and enterprise risk management simultaneously. A convergence of technology, data science, behavioral research and computing power are no longer wishful thinking about the future. The future is here but in order to fully harness the power of these technologies and the benefits possible IT security professionals and risk managers, in general, need a guidepost for comprehensive change. The cognitive risk framework for cybersecurity is the first of many advances that will change how organizations manage risk now and in the future in fundamental and profound ways few have dared to imagine.
Simplicity may conjure up thoughts of inner peace and contemplative musings about self-actualization, but that is not the kind of simplicity I am referring to. The concept of simplicity that I think is really interesting is the challenge of making the complex simple. I am referring to the kind of simplicity that Steve Jobs imagined when he changed how we use technology.
Jobs redesigned how our brains interact with technology without our realizing we were participating in a brain hack! The ecosystem Apple created with the Mac and mobile devices via the Apple Store is a stroke of genius and an answer to an interesting problem. How to make technology so simple everyone on the planet can use it right out of the box?
“The reason that Apple is able to create products like the iPad is because we’ve always tried to be at the intersection of technology and the liberal arts. To be able to get the best of both. To make extremely advanced products from a technology point of view, but also have them be intuitive easy-to-use, fun-to-use, so that they really fit the users. The users don’t have to come to them, they come to the user. And it’s the combination of these two things that I think has let us make the kind of creative products like the iPad,” quote from Steve Jobs 2010’s introduction of the iPad.
Supposedly, the idea of a smart phone had been discussed long before Apple created the first iPhone but no one was able to put all the pieces together in the way Jobs did. The lesson from Apple’s success should not be that simplicity is too hard to conceive; instead, we must reframe simplicity as the end goal.
By reframing simplicity as the end goal, Jobs was able to see how multiple devices, such as, the Walkman (remember those?), cameras, and phones could be integrated seamlessly into one device? Jobs showed how a focus on solving the problems that led to the cause of poor customer experience helps create higher profits, customer loyalty and shareholder value; not the other way around. More importantly than the technology, Jobs chose not to control how the devices were used which harnessed yet another eco-system of developers and innovators who shared in Apple’s ascent to become the most profitable company on the planet. In other words, simplification led to organic iterations of new services spawning demand globally for all things Apple.
This raises very interesting questions about how we deal with risks or solve complex problems that appear to be intractable. If complexity is a product of our own design what can we learn from Apple’s lessons in simplicity? You might be surprised that simplicity is a topic being studied and tested in real world scenarios.
About 5 years ago a new website called the SimplicityIndex was created by Siegel+Gale, a global brand strategy, design and experience firm to understand the role simplicity plays in brand awareness and loyalty. The Simplicity Index explains how customers perceive the simplicity of a company’s products and services: Easy to Understand; Transparent and Honest; Making Customers Feel Valued; Innovative and Fresh; and, Useful to Customers. The simplicity attributes of brand leads to measurable benefits in higher profitability, customer loyalty and premium pricing because of the perceived value.
Simplicity can be quantified and measured in real returns to organizations!
The power of simplicity is much bigger than a product strategy! Consider how risk management could be transformed if internal controls and compliance were redesigned to make it simple for employees to get their work done or follow the rules? Simplicity requires that we ask non-intuitive questions such as why must we continue to operate the way we do or what barriers to simplicity exist for customers and employees? Is it time to reconsider how the attributes from the Simplicity Index serve as the end game, not a mission statement with no real strategy of execution?
While you ponder those questions we should also ask why complexity is the norm and not simplicity. There are no simple answers but there are examples from network engineering of how overly complex network security design leads to vulnerabilities in cybersecurity.
The concept of Robust Yet Fragile
Engineers of computer networks are well versed in how complexity builds as well-meaning security professionals add controls and policies in response to threats and weaknesses without considering the impact to network fragility over time.
John Doyle, the John G Braun Professor of Control and Dynamical Systems, Electrical Engineering, and BioEngineering at the California Institute of Technology, introduced the concept of “Robust Yet Fragile” (“RYF”) paradigm to explain the five components of network design used to build a robust system.
Each design component is built on the concept of adding robustness to networks to handle today’s evolving business needs. “Reliability is robustness to component failures. Efficiency is robustness to resource scarcity. Scalability is robustness to changes in the size and complexity of the system as a whole. Modularity is robustness to structure component rearrangements. Evolvability is robustness of lineages to changes on longtime scales.
The graph in the above exhibit describes the optimal point of robust network design. “Like all systems of equilibrium, the point at which robust network design leads to unnecessary complexity is the paradox faced by security professionals and systems architects. Systems, such as the Internet, are robust for a single point of failure yet fragile to a targeted attack. As networks bolt on more stuff to build scale the weight of all that stuff becomes more risky,” according to Doyle.
Doyle’s warnings about internet security also applies to enterprise risk management. Does anyone really believe that every employee understands how to operationalize all of the myriad policies and procedures put into effect each year? If so, you may be operating in the Domain of the Fragile and not aware of the vulnerabilities lurking around the corner.
How does an organization reframe simplicity?
The answer to that question is different by industry and organizational culture. A better way to answer the question is to pose new questions for you to consider in your organization. For example, has the cost of critical operational functions increased at a higher rate than the benefits? How difficult is it for management to get timely answers about customer profitability, enterprise risk or financial performance? Are you losing customers because you are difficult to do business with? Are your employees empowered to solve risks on their own or given the tools to improve the customer experience? As you can see the list of possibilities are endless; however, if you are not aware of the answers to these questions you are operating in the Domain of the Fragile.
Board governance is one place where the example of simplicity can be modeled from the top down. Directors have an opportunity to reframe success and reduce risk with a focus on simplicity. Simplicity is not just a focus on Less but a renewed focus on Better. Simplicity is not about doing “more” with “less”; it’s about doing less to achieve more!
As you consider new strategies for 2016 and beyond how you reframe simplicity may be the difference in success or failure for years to come.
R.I.S.K. is the next generation chief risk/audit/compliance/IT security officer who is capable of processing billions of bits of data, analyzing behavioral patterns, assess changes in internal controls and tackle cyber risks within seconds of an attack. R.I.S.K. does not command a salary, go on vacation, require a pension or healthcare benefits nor complain about not having enough budget or resources to get their job done.
What is R.I.S.K.? Risk Intelligent Systems Knowledgeware is a concept that I created to describe a collection of informatics applications that are in development today designed to tackle the challenge of tomorrow’s complex risk problems. If you think this is some far-fetched science fiction story about risk management you simply have not done your homework. Let me explain why risk management, as you know it today, will never be the same and is going through a major transformation never before seen.
Intelligence and security informatics (“ISI”) is defined as the development of advanced information technologies, systems, algorithms, and databases for international, national, and homeland security-related applications, through an integrated technological, organizational, and policy-based approach. Academics, military researchers, systems programmers and information security engineers are exploring a range of advanced technologies to address tomorrow’s threats. Disparate teams from around the world are separately; and in collaborative partnership, working on first generation smart systems to redefine how risk management and cyber security will be prosecuted in the very near future. While it is true that much of this research is very early stage it is also true that practical applications are being used today.
What is driving this change? Every organization is impacted by the speed of change and volumes of data generated by regulation and our 24/7 online, on-all-the-time, networked environment. Whether you work in a government agency, small business or global corporate enterprise humans candidly cannot keep up without the assistance of technology. It would be naïve to assume that risk, audit, IT security and compliance professionals have the ability to assess the health of an entire organization by reviewing a fraction of the internal controls and enterprise threats that endlessly flow through every firm.
Risk professionals spend 80% or more of their time focused on high frequency, low impact risks because it is easy to capture yet only creates a false sense of security. The phenomenon is called cognitive overload and creates a distraction from the true risks that threaten organizations. This is the primary reason organizations are “surprised” when a major control failure disrupts business or security professionals fail to keep up with cyber threats. Conventional risk practice is not enough! Unfortunately, risk professionals cling to ineffective risk practice without questioning outcomes or seeking alternatives.
So what are the implications of this transformation in risk management? First of all, it is important to understand that this change has already begun and will speed up rapidly as new technology is brought to bear to address risks. Open source intelligence is increasingly being used in security related applications. Hundreds of cyber security vendor applications have been launched in the last 3-5 years and behavioral defense systems have been deployed to identify patterns of insider threats to proprietary corporate data.
As these systems and their developers learn from their early stage experience more advanced applications will be deployed very rapidly. Artificial intelligence and machine learning are playing a larger role in cybersecurity, which can in theory help companies identify risks and anticipate problems before they occur. The idea is to create software that can adapt and evolve to combat ever-changing attack strategies, or identify patterns of suspicious behavior.
Traditional security mechanisms have leveraged rule, pattern, signature and algorithm-based approaches to detect threats, and that’s a problem, according to Paul Stokes, CIO of the University of Victoria in British Columbia. “These approaches require constant care and feeding to identify and mitigate security threats,” he said. “I think machine learning changes the game.”
The risk professional of the future will be more defined in skill set and come from a diverse set of deep domain expertise beyond audit, legal, operations or generalist oriented backgrounds. Risk engineers will increasingly become a new title bestowed on security professionals able to design or deploy systems with intelligence custom fit to the organization’s risk. The cost of risk, compliance and audit will be streamlined and spread across resources more effectively targeting real threats to the enterprise. These changes were unimaginable a mere 5 years ago but are becoming a reality today.
The question is are you prepared or do you ignore the change until you are replaced by R.I.S.K.?
Organizations are overlooking their best candidates for job openings and leadership positions while adding hidden costs and risks to the firm in the process. Many hiring managers and human resource recruiters have accepted the often stated illusion of hiring the best athlete for the opening which too often is perceived to be an external candidate with more experience from a competitor firm.
In a recent study by Matthew Bidwell, Wharton School’s Penn State University, “external hires will initially perform worse than workers entering the job from inside the firm and have higher exit rates, yet they will be paid [18%] more, on average, and have stronger observable indicators of ability as measured by experience and education.” The best athlete myth is called the Halo Affect by cognitive scientists. External talent is perceived to possess skills not evidenced by empirical data.
Blame professional sports for creating many of these illusions. We see our favorite sports teams pay millions to athletes in a never ending search for the “right mix” of position talent to win the national championship of the team sport. What we don’t fully understand is the failure rate of this flawed philosophy or the poor odds of success in this strategy.
Professor Bidwell’s study points out that “workers promoted into jobs [cost less] and have significantly better performance for the first two years than workers hired into similar jobs and [have] lower rates of voluntary and involuntary exit.” Few firms calculate the risk or cost of turnover of external hires but it they did they would be shocked by the negative results.
So how does an organization overcome this bias in talent management and avoid these inherent risks?
Reallocate resources into internal staff development to acquire the perceived skills gap that is missing. Organizations must consider human resources a strategic asset that is refined and developed over time. Behavioral science has established that it takes approximately 10,000 hours to perfect a skill or become an expert. Manage those hours to the benefit of the firm! New and existing employees should have a path to expertise that ensures the organization is creating the talent it will need for the future success of the firm.
Talent risk is a self-inflicted wound that can only be overcome by changing how organizations perceive its internal talent. According to a study by CareerXRoads, 58% of new job openings were filled by external hires. While it is not always possible to fill existing openings with internal staff its takes new hires 2 – 5 years to become proficient in their new roles in any organization.
Talent risk is a strategic risk every firm can manage. Firms can save money and build sustainable risk cultures by hiring and promoting its internal talent.
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