The proverbial Computing Troika that KuppingerCole has been writing about for years does not show any signs of slowing down. The technological trio of Cloud, Mobile and Social computing, as well as their younger cousin, the Internet of Things, have profoundly changed the way our society works. Modern enterprises were quickly to adopt these technologies, which create great new business models, open up numerous communication paths to their partners and customers, and, last but not least, provide substantial cost savings. We are moving full speed ahead towards the Digital Era, and the future is full of promise. Or is it?
Unfortunately, the Digital Transformation does not only enable a whole range of business prospects, it also exposes the company’s most valuable assets to new security risks. Since those digital assets are nowadays often located somewhere in the cloud, with an increasing number of people and devices accessing them anywhere at any time, the traditional notion of security perimeter ceases to exist, and traditional security tools cannot keep up with the new sophisticated cyberattack methods.
In the recent years, the industry has come up with a new generation of security solutions, which KuppingerCole has dubbed “Real-Time Security Intelligence”. Thanks to a technological breakthrough that finally commoditized Big Data analytics technologies previously only affordable to large corporations, it became possible to collect, store, and analyze huge amounts of security data across multiple sources in real time. Various correlation algorithms have been implemented to find patterns in the data, as well as to detect anomalies, which in most cases indicate a certain kind of malicious activities.
Such security analytics solutions have been hailed (quite justifiably) by the media as the ultimate solution to most modern cybersecurity problems. Some even go as far as referring to these technologies as “machine learning” or even “artificial intelligence”. It should be noted however, that detecting patterns and anomalies in data sets has very little to do with true intelligence – in fact, if the “IQ level” of a traditional signature-based antivirus can be compared to that of an insect, then the correlation engine of a modern security analytics solution is about as “smart” as a frog catching flies.
Unfortunately, the strong artificial intelligence, comparable in skill and flexibility to a human, is still purely a subject of theoretical academic research. Its practical applications, however, are no longer a science fiction topic. To the contrary, these applied cognitive technologies have been actively developed for quite some time already, and the exponential growth of cloud computing has been a major boost for their further development in the recent years. Such technologies as computer vision, speech recognition, natural language processing or machine learning have found practical use in many industries, and cybersecurity is the most recent field where they promise to achieve a major breakthrough.
You see, the biggest problem information security is now facing has nothing to do with computers. In fact, the vast majority (over 80%) of security-related information in the world remains completely inaccessible to computers: it exists only in an unstructured form spread across tens of thousands of publications, conference presentations, forensic reports and other sources – spoken, written or visual.
Only a human can read and interpret those data sources, but we do not have nearly enough humans trained as security analysts to cope with the amount of new security information produced daily.
This is where Cognitive Security, a new practical application of existing cognitive technologies, comes into play. A cognitive security solution would be able to utilize natural language processing and machine learning methods to analyze both structured and unstructured security information the way humans do. It would be able to read texts (or even see pictures and listen to speeches) and not just recognize patterns within them, but be able to interpret and organize the information, explain its meaning, postulate hypotheses and provide reasoning based on evidence.
This may feel like science fiction to some, but the first practical cognitive security solutions are already appearing on the market. A major player and one of the pioneers in this field is undoubtedly IBM with their Watson platform. Originally created back in 2005 to compete with human players in the game of Jeopardy, over the years Watson has expanded significantly and found many practical applications in business analytics, government, legal and even healthcare services.
In May 2016, IBM has announced Watson for Cyber Security, a completely new field for their natural language processing and machine learning platform. However, IBM is definitely not a newcomer in cyber security. In fact, their own X-Force research library is being used as the primary source of security information to be fed into the specialized instance of the platform running in the cloud. Although the learning process is still in progress, the ultimate goal is to process all of those 80% of security intelligence data and make it available in structured form.
Of course, Watson for Cyber Security will never replace a human security analyst, but that is not its goal. First, making this “dark security data” accessible for automated processing by current security analytics solutions can greatly improve their efficiency as well as provide additional external threat intelligence. Second, cognitive security would provide analysts with powerful decision support tools, simplifying and speeding up their work and thus reducing the skills gap haunting the security industry today. In the future, the same cognitive technologies may be also applied to a company’s own digital assets to provide better analytics and information protection. Potentially, they may even make developing malware capable of evading detection too costly, thus turning the tide of the ongoing battle against cybercrime.
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