The emerging field of artificial intelligence technologies can lead to a revolution in cybersecurity.
Detecting And Blocking Hacked IoT Devices
Any individual and business needs to ensure securing their digital assets, whether they are looking to protect company's intellectual property, personal photos, customers' sensitive data, or anything else that can affect business continuity or individual reputation.
According to Venture Beat, while billions of dollars are spent on cybersecurity, the magnitude of breaches and the number of reported cyberattacks still keep rising. Fortunately, there are hopes this situation will suddenly change.
There are many ways to take advantage of the predictive power of artificial intelligence (AI) for developing new advanced cybersecurity apps. Artificial intelligence might give security experts as well as businesses and individuals the upper hand in the field by implementing crucial keys of cyberdefense innovation.
Cisco estimates that the number of Internet of Things (IoT) connected devices in the world will increase to 50 billion by 2020 from 15 billion today. Due to limited software and hardware resources, most of these devices do not have basic security measures.
The recent massive denial of service attack issued against KerbsOnSecurity was a strong demonstration of the power of hacked IoT devices. But the fact that the source code for the Mirai malware used in the attack was soon released to the public is even more frightening.
AI technologies have one of most prominent arenas of developing in the field of IoT security. Lightweight AI-based prediction models that can reside and operate autonomously even on devices with low computing power are able to enable detection and blocking of suspicious activity in real time on either at network level or on the device. Several startup companies implement AI technologies for IoT security applications, including PFP Cybersecurity, CyberX and Dojo-Labs.
Preventing Execution Of Malicious Files And Software
One of the leading cyberattack vectors remain the file-based attacks. These file-based cyberattack most commonly use Acrobat Reader (.pdf), executables (.exe) and MS Office file types.
A new malicious file with different signature but the same malicious intent can be created with just a small change in line of code. These small changes in code cannot be detected by advanced heuristic-based endpoint detection and response (EDR) solutions, legacy signature-based antivirus programs, as well as even network level solutions such as sandboxing.
Such problem can be solved by harnessing AI power and there are already a few startups that address this. They leverage the AI's great capability to evaluate millions of features per suspicious file. This way can be detected even the slightest code mutations. Among the leader companies in implementing file-based AI security are included Cylance and Invincea.
Quantifying Risks
Another challenging task within the field of cybersecurity is quantifying organizations' cyber risks. This is mainly due to the vast number of variables that need assessed and the lack of historic data.
Many of today's organizations as well as cyber insurers and other third parties that want to assess these organizations must go through a tedious cyber risk assessment process if they are interested in quantifying their risks. This assessment process is mainly based on questionnaires that evaluate an organization's governance and risk culture as well as qualitative measures of compliance with available cybersecurity standards.
For a genuine representation of cyber risks, this kind of approach might be insufficient. A better way is to take advantage of AI technologies' ability to generate predictions by processing millions of data points. This way, cyber insurers and organizations can come at the most accurate cyber risks estimation. Among the startup companies that are approaching this task are included Security Scorecard and BitSight.
