Artificial Intelligence, or AI, is no longer the exclusive tool of well-funded government entities and defense contractors, let alone a plot device in science fiction film and literature. Instead, AI is becoming as ubiquitous as the personal computer. The opportunities of what AI can do for internal audit are almost as endless as the challenges this disruptive technology represents.
To understand how AI will influence internal audit, we must first understand what AI is.
The concept of AI—a technology that can perceive the world directly and respond to what it perceives—is often attributed to Alan Turing, though the term “Artificial Intelligence” was coined much later in 1956 at Dartmouth College, in Hanover, New Hampshire. Turing was a British scientist who developed the machine that cracked the Nazis’ Enigma code. Turing thought of AI as a machine that could convince a human that it also was human. Turing’s humble description of AI is as simple as it is elegant. Fast-forward some 60 years and AI is all around us and being applied in novel ways almost every day. Just consider autonomous self- driving vehicles, facial recognition systems that can spot a fugitive in a crowd, search engines that tailor our online experience, and even Pandora, which analyzes our tastes in music.
Today, in practice and in theory, there are four types of AI. Type I AI may be best represented by IBM’s Deep Blue, a chess-playing computer that made headlines in 1996 when it won a match against Russian chess champion Gary Kasparov. Type I AI is reactive. Deep Blue can beat a chess champion because it evaluates every piece on the chessboard, calculates all possible moves, then predicts the optimal move among all possibilities. Type I AI is really nothing more than a super calculator, processing data much faster than the human mind can. This is what gives Type I AI an advantage over humans.
Type II AI, which we find in autonomous cars, is also reactive. For example, it applies brakes when it predicts a collision; but, it has a low form of memory as well. Type II AI can briefly remember details, such as the speed of oncoming traffic or the distance between the car and a bicyclist. However, this memory is volatile. When the situation has passed, Type II AI deletes the data from its memory and moves on to the next challenge down the road.
Type II AI's simple form of memory management and the ability to “learn” from the world in which it resides is a significant advancement. The leap from Type II AI to Type III AI has yet to occur. Type III AI will not only incorporate the awareness of the world around it, but will also be able to predict the responses and motivations of other entities and objects, and understand that emotions and thoughts are the drivers of behavior. Taking the autonomous car analogy to the next step, Type III AI vehicles will interact with the driver. By conducting a simple assessment of the driver’s emotions, the AI will be able to suggest a soothing playlist to ease the driver's tensions during his or her commute, reducing the likelihood of aggressive driving. Lastly, Type IV AI–a milestone that will likely be reached at some point over the next 20 or 30 years—will be self-aware. Not only will Type IV AI soothe the driver, it will interact with the driver as if it were another human riding along for the drive; think of “HAL” in Arthur C. Clarke’s 2001: A Space Odyssey.
So what does this all mean to internal auditors?
While it may be a bit premature to predict AI’s impact on the internal audit profession, AI is already being used to predict control failures in institutions with robust cybersecurity programs. When malicious code is detected and certain conditions are met, AI-enabled devices can either divert the malicious traffic away from sensitive data, or even shut off access completely until an incident response team has had time to investigate the nature of the attack and take appropriate actions. This may seem a rather rudimentary use of AI, but in large financial institutions or manufacturing facilities, minutes count—and equal dollars. Allowing AI to cut off access to a line of business that may cost the company money (and its reputation) is a significant leap of faith, and not for the faint of heart. Next generation AI-enabled devices will have even more capabilities, including behavioral analysis, to predict a user’s intentions before gaining access to data.
In the future, internal audit staff will no doubt train AI to seek conditions that require deeper analysis, or even predict when a control will fail. Yet AI will be able to facilitate the internal audit process in other ways. Consider AI’s role in data quality. Advances in inexpensive data storage (e.g., the cloud) have allowed the creation and aggregation of volumes of data subject to internal audit, making the testing of the data’s completeness, integrity, and reliability a challenging task considering the sheer volume of data. Future AI will be able to continuously monitor this data, alerting internal auditors not only of the status of data in both storage and motion, but also of potential fraud and disclosures.
The analysis won’t stop there.
AI will measure the performance of the data in meeting organizational objectives, and suggest where efficiencies can be gained by focusing technical and human resources to where the greatest risks to the organization exist in near real-time. This will allow internal auditors to develop a common operating picture of the day-to-day activities in their business environments, alerting internal audit when something doesn’t quite look right and requires further investigation.
As promising as AI is, the technology comes with some ethical considerations. Because AI is created by humans, it is not always vacant of human flaws. For instance, AI can become unpredictably biased. AI used in facial recognition systems has made racial judgments based on certain common facial characteristics. In addition, AI that gathers data from multiple sources that span a person’s financial status, credit status, education, and individual likes and dislikes could be used to profile certain groups for nefarious intentions. Moreover, AI has the potential to be weaponized in ways that we have yet to comprehend.
There is also the question of how internal auditors will be able to audit AI. Keeping AI safe from internal fraudsters and external adversaries is going to be paramount. AI’s ability to think and act faster than humans will challenge all of us to create novel ways of designing and testing controls to measure AI’s performance. This, in turn, will likely make partnerships with consultants that can fill knowledge gaps even more valuable.
Challenges and pitfalls aside, AI will likely have a tremendous positive effect on the internal audit profession by simultaneously identifying risks and evaluating processes and control design. In fact, it is quite possible that the first adopters of AI in many organizations may not be the cybersecurity departments at all, but rather the internal auditor’s office. As a result, future internal auditors will become highly technical professionals and perhaps trailblazers in this new and amazing technology.