In the physical security industry, the advent of artificial intelligence (AI) and deep learning is most commonly associated with potential improvements in video analytics performance. However, AI is also applicable to a variety of content analytics beyond video. This is part three of our 'AI in Physical Security' series.
It will be interesting to watch how companies that take the next step beyond proving viability for security purposes to deliver true business applications to the market. Right now, we’re seeing organisations working hard to develop content analytics that perform in an effective, efficient and accurate manner.
Enterprise software companies
This shift will create a huge disruption in our industry and cause further consolidation
Many of these organisations are true AI and/or computer vision companies, and they are spending a lot of money developing very advanced algorithms. However, there’s still work to be done identifying the real benefit of these analytics for customers as part of comprehensive business intelligence solutions. Until that happens, and customers understand how those benefits apply to them directly, adoption will continue to be lower than all the marketing hype would suggest.
Because data analytics are becoming such a significant component of today’s ‘big data’ solutions, watch for a number of large, enterprise software companies to start focusing on the security industry. This shift will create a huge disruption in our industry and cause further consolidation.
Analysing mobile endpoints
There is also a potential for machine learning to enable cybersecurity companies to predict the nature of future attacks based on past behaviour, similar to how Netflix displays what you want to watch based on what you’ve previously viewed. According to Jack Gold, president and principal analyst at J. Gold Associates, this innovation can assist cyber companies to transition away from a ‘signature-based’ system to detect malware. Instead, he sees more companies adopting a machine learning approach that aims to analyse past incidents in a broader manner and aggregate information from a multitude of sources.
| A main function of AI is to analyse past incidents in a broader manner and aggregate information from a multitude of sources |
Specifically, some machine learning applications for cybersecurity are effective at doing the following: detecting malicious activity, helping security officers determine what tasks they need to complete in an investigation process, analysing mobile endpoints, decreasing the number of false positive threats, automating repetitive tasks like interrupting ransomware, and potentially closing some zero-day vulnerabilities.
Android mobile endpoints
A number of tech giants have invested in these capabilities recently, including Google, which is employing machine learning to help protect Android mobile endpoints. Amazon also bought a startup called harvest.AI to help it aggregate and better understand data located on the S3 cloud storage service.
Machine learning can help cybersecurity efforts, but it can’t replace many important functions
Ultimately, machine learning can help cybersecurity efforts, but it can’t replace many important functions. There will always be sophisticated attacks that no machine learning algorithm will be able to find. Pairing human intellect with machine technology is the best approach. In another application, AI-driven robots can be deployed for security in places where it may not be feasible to have a human patrol, such as the outskirts of a vital electric substation located hundreds of miles from the nearest town.
Evolution of artificial intelligence
A robot can easily traverse the harsh terrain and notify authorities when something is amiss. Another use is during disaster recovery efforts. Robots don’t get tired, and they don’t have to use the bathroom, eat or take a break.
With the abilities afforded by AI, robots can also navigate any designated area autonomously to keep an eye out for suspicious behaviour or alert first responders to those who may need aid. In situations where health and safety concerns preclude the ability of having a human to watch the site, such as at toxic waste dumps, robots can be deployed.
Although drones still largely require a human operator to chart their flight paths and control their movements, the evolution of artificial intelligence is also revolutionising the capabilities of machines to work autonomously.
If you missed part two, see it here. Or, to start from part one, click here.