NATURAL LANGUAGE PROCESSING continues to find its way into unexpected corners. This time, it's phishing emails. In a small study, researchers found that they could use the deep learning language model GPT-3, along with other AI-as-a-service platforms, to significantly lower the barrier to entry for crafting spearphishing campaigns at a massive scale.
Coined by Gartner, AIOps—i.e. artificial intelligence for IT operations—is the application of artificial intelligence (AI) capabilities, such as natural language processing and machine learning models, to automate and streamline operational workflows. By aggregating data in real-time, AIOps platforms can make predictions around operational hazards, such as a data breach, which can either kick off a prescriptive action automatically, like a defense protocol, or alert security teams to action on an urgent issue more immediately. These tools are typically integrated into DevOps and DevSecOps teams to help with performance monitoring and reduce mean-time-to-know (MTTK).
The demand for AIOps has only grown with the increased business focus on digital transformation initiatives. While the use of virtual machines, container-based microservices and shared multi-tenant infrastructure have accelerated application development, it has unfortunately come at the expense of operational efficiency as each app has its own set of data. AIOps attempts to break down the operational silos by aggregating this data and providing more transparency and insight to it organizations. This, in turn, allows businesses to reduce costs and improve decision-making to make progress against goals.
A scientist who wrote a leading textbook on artificial intelligence has said experts are “spooked” by their own success in the field, comparing the advance of AI to the development of the atom bomb.
Prof Stuart Russell, the founder of the Center for Human-Compatible Artificial Intelligence at the University of California, Berkeley, said most experts believed that machines more intelligent than humans would be developed this century, and he called for international treaties to regulate the development of the technology.
We have all read stories of facial recognition software that fails to recognize dark-skinned faces, or robo-loan officers that deny mortgages to certain groups. As a growing body of research has made clear, algorithms created by non-representative groups have resulted in AI that perpetuates the inequities already prevalent in our society. As more companies rely more heavily on data and AI, these problems of algorithmic discrimination may only become worse.
Most companies know this by now. What they’re trying to figure out is: how can they avoid becoming yet another bad example?
The short answer is, thinking critically about the data you’re collecting and how you’re using it needs to be everyone’s job. Expanding the circle of who is in the room helping to question, build, and monitor algorithms is the only way that we will develop responsible AI. Doing that work requires data literacy — the ability to parse and organize complex data, interpret and summarize information, develop predictions, or appreciate the ethical implications of algorithms. Like math, it can be learned in beginner and advanced modes, spans multiple disciplines, and is often more practical than academic.
The exponential development of computing devices has expanded access capabilities for cybercriminals to detect and exploit vulnerabilities in innovative ways. With a medium-grade smartphone now more powerful than the most advanced computers were only a few years ago, for example, cybercriminals can launch powerful and sophisticated attacks at a relatively low cost from a mobile unit while also working from home.
That ease of access goes some way to explain why there is a cyberattack every 39 seconds. As industrial organizations embrace digitalization, inadequate security protection can open up their systems to malicious actors. Criminals today use a wide variety of methods, from commonly used techniques such as phishing and computerized password hacking to more sophisticated operations such as watering hole attacks that deliver malware to visitors.
Increasingly, the same innovative technology that is being used to deliver innovative solutions for common benefit is being deployed in destructive ways to inflict catastrophic damage to infrastructure, business systems, and, ultimately, the citizenry.
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