qkiser ) The Power of AI Technology"

 The Power of A I Technology"

Artificial intelligence (A I) is truly a revolutionary feat of computer science, set to become a core component of all modern software over the coming years and decades. This presents a threat but also an opportunity. A I will be deployed to augment both defensive and offensive cyber operations. Additionally, new means of cyber attack will be invented to take advantage of the particular weaknesses of A I technology. Finally, the importance of data will be amplified by A I’s appetite for large amounts of training data, redefining how we must think about data protection. Prudent governance at the global level will be essential to ensure that this era-defining technology will bring about broadly shared safety and prosperity.

A I and Big Data

In general terms, A I refers to computational tools that are able to substitute for human intelligence in the performance of certain tasks. This technology is currently advancing at a breakneck pace, much like the exponential growth experienced by database technology in the late twentieth century. Databases have grown to become the core infrastructure that drives enterprise-level software. Similarly, most of the new value added from software over the coming decades is expected to be driven, at least in part, by A I.

Within the last decade, databases have evolved significantly in order to handle the new phenomenon dubbed “big data.” This refers to the unprecedented size and global scale of modern data sets, largely gathered from the computer systems that have come to mediate nearly every aspect of daily life. For instance, YouTube receives over 400 hours of video content each minute (Brouwer 2015).

Big data and A I have a special relationship. Recent breakthroughs in A I development stem mostly from “machine learning.” Instead of dictating a static set of directions for an A I to follow, this technique trains A I by using large data sets. For example, A I chatbots can be trained on data sets containing text recordings of human conversation collected from messenger apps to learn how to understand what humans say, and to come up with appropriate responses (Pandey 2018). One could say that big data is the raw material that fuels A I algorithms and models.

The main constraint on innovation is no longer the difficulty in recording and storing information, but the finding of useful insights among the sheer abundance of data now being collected. A I can notice patterns in mammoth data sets that are beyond the ability of human perception to detect. In this way, the adoption of A I technology can make even mundane and seemingly trivial data valuable. For instance, researchers have trained computer models to identify an individual’s personality traits more accurately than their friends can, based exclusively on what Facebook posts the individual had liked (Wu, Kosinski and Stillwell 2015).

A I and Cyber Security

Hardly a day passes without a news story about a high-profile data breach or a cyber attack costing millions of dollars in damages. Cyber losses are difficult to estimate, but the International Monetary Fund places them in the range of US$100–$250 billion annually for the global financial sector (Lagarde 2012). Furthermore, with the ever-growing pervasiveness of computers, mobile devices, servers and smart devices, the aggregate threat exposure grows each day. While the business and policy communities are still struggling to wrap their heads around the cyber realm’s newfound importance, the application of A I to cyber security is heralding even greater changes.

One of the essential purposes of A I is to automate tasks that previously would have required human intelligence. Cutting down on the labour resources an organization must employ to complete a project, or the time an individual must devote to routine tasks, enables tremendous gains in efficiency. For instance, chatbots can be used to field customer service questions, and medical assistant A I can be used to diagnose diseases based on patients’ symptoms.

In a simplified model of how A I could be applied to cyber defence, log lines of recorded activity from servers and network components can be labelled as “hostile” or “non-hostile,” and an A I system can be trained using this data set to classify future observations into one of those two classes. The system can then act as an automated sentinel, singling out unusual observations from the vast background noise of normal activity.

This kind of automated cyber defence is necessary to deal with the overwhelming level of activity that must now be monitored. We have passed the level of complexity at which defence and identification of hostile actors can be performed without the use of A I. Going forward, only systems that apply A I to the task will be able to deal with the complexity and speed found in the cyber security environment. 

Continuously retraining such A I models is essential, since just as A I is used to prevent attacks, hostile actors of all types are also using A I to recognize patterns and identify the weak points of their potential targets. The state of play is a battlefield where each side is continually probing the other and devising new defences or new forms of attack, and this battlefield is changing by the minute.

Perhaps the most effective weapon in a hacker’s arsenal is “spear phishing” — using personal information gathered about an intended target to send them an individually tailored message. An email seemingly written by a friend, or a link related to the target’s hobbies, has a high chance of avoiding suspicion. This method is currently quite labour intensive, requiring the would-be hacker to manually conduct detailed research on each of their intended targets. However, an A I similar to chatbots could be used to automatically construct personalized messages for large numbers of people using data obtained from their browsing history, emails and tweets (Brundage et al. 2018, 18). In this way, a hostile actor could use A I to dramatically scale up their offensive operations.

A I can also be used to automate the search for security flaws in software, such as “zero-day vulnerabilities.” This can be done with either lawful or criminal intent. Software designers could use A I to test for holes in their product’s security, just as criminals search for undiscovered exploits in operating systems.

The New Value of Data

A I technology will alter the cyber security environment in yet another way as its hunger for data changes what kind of information constitutes a useful asset, transforming troves of information that would not previously have been of interest into tempting targets for hostile actors.

While some cyber attacks aim solely to disrupt, inflict damage or wreak havoc, many intend to capture strategic assets such as intellectual property. Increasingly, aggressors in cyberspace are playing a long-term game, looking to acquire data for purposes yet unknown. The ability of A I systems to make use of even innocuous data is giving rise to the tactic of “data hoovering” — harvesting whatever information one can and storing it for future strategic use, even if that use is not well defined at present.

A recent report from The New York Times illustrates an example of this strategy in action (Sanger et al. 2018). The report notes that the Chinese government has been implicated in the theft of personal data from more than 500 million customers of the Marriott hotel chain. Although commonly the chief concern regarding data breaches is the potential misuse of financial information, in this case the information could be used to track down suspected spies by examining travel habits, or to track and detain individuals to use them as bargaining chips in other matters.

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