In a world of ever-increasing data, artificial intelligence (AI) and machine learning (ML) technologies are amazing force-multipliers for security professionals, particularly analysts and intelligence teams who are required to collect and analyze large amounts of information.
Though we aren’t there yet, it’s easy to see artificial intelligence being standard in every analyst’s toolkit in the near future. However, they need more tools in their kit to provide critical elements such as context and interpretation of the “so what” from what AI tools might be displaying. So, while AI might increasingly be a necessity, analysts aren’t in danger of being put out of business for the foreseeable future.
Language Evolution
Many threat actors know that law enforcement and social media companies are using AI to track their behaviors. So, they continually evolve their language and behaviors to evade this technology (leetspeak is one example of this). Only a human with an understanding of the digital, and sometimes physical, environment that these threat actors are operating in can fully understand the significance of their evolving communicative behaviors and can provide guidance on what might happen next.
Necessary Context
While AI can use data and math to tell us when something is abnormal, a human is still needed to interpret why this abnormality is present, how significant it may be to an organization, and what to do about it. Technology, as of now, can’t provide the “so what” factor that many organizations desire. You might have all the data you could want, but what does that mean for your organization, your factory in India, or your personnel in Brazil? That must come from a human with expertise in your sector and/or region.
Data Gaps
Artificial intelligence and machine learning are only as good as the data inputs and subsequent machine learning training facilitated through those inputs. This matters to security and risk professionals because there are still many parts of the world where there is a dearth of data and news sources, or those sources may be biased and/or censored. Only a human can realistically fill these gaps through experience and on-the-ground insights.
Augmented Analytics
The emergence of augmented analytics – blending AI, ML, and expert analysis – brings all the necessary puzzle pieces together to form a full picture of the risk and threat landscape. There are many ways that analysts can collect the information they need to inform and guide leaders on potential threats. AI and ML tools are an exciting way to gather data quickly and easily, but humans and their expertise are still very much needed to ensure that consumers of intelligence products have critical context around the data.
