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Since the dawn of the Industrial Revolution and the exponential growth of automation in the workplace, the working man (from blue collar to CEO) has sometimes feared being replaced.
The leaps and bounds AI has taken in the last 20 years has exacerbated these fears for even the most niche and technically gifted IT professional; Cyber Security experts among them. What role could AI and Machine Learning play in Security Posture? Will there always be a place for the human actor? The ‘human element’ of any system is often its weakest part so is it time to phase them out?
The Upside:
Threat Hunting; a term that sounds like it belongs in sci-fi because it almost does! Threats are recognised using signatures or indicators of compromise in traditional security procedures. This method may be successful against threats that have already been experienced, but it is ineffective against threats that have not yet been identified.
About 90% of threats may be detected using signature-based strategies. Artificial intelligence (AI) can improve detection rates up to 95%, however there will be a huge number of false positives. The best course of action would be to use both conventional techniques and AI. As a result, there will be no false positives and a 100% detection rate.
Network Security is additionally vital and includes two time-consuming components: developing Security Policies and comprehending the Network Topology of a company. By studying network traffic patterns and advising both functional grouping of workloads and security policy, businesses can use AI to enhance network security.
Vulnerability Management is also a critical component. Traditional vulnerability databases are essential for managing and containing known vulnerabilities, but AI and machine learning techniques like User and Event Behavioral Analytics (UEBA) can examine normal behavior of user accounts, endpoints, and servers and spot anomalous behavior that may indicate a zero-day unknown attack. Even before vulnerabilities are formally identified and addressed, this can aid in protecting companies.
The Downside:
Fuzzing: also known as neural fuzzing—is the technique of subjecting software to extensive random input testing in order to find weaknesses. AI is used in neural fuzzing to swiftly test a lot of random inputs. Fuzzing does, however, have a positive side. By gathering data using the strength of neural networks, hackers can discover the flaws in a target system. Microsoft created a mechanism to implement this strategy in order to enhance their software, producing more secure code that is more difficult to breach.
Resources: In order to create and operate AI systems, businesses must spend a significant amount of time and money on resources like processing power, memory, and data.
Data Sets: Learning data sets are used to train AI models. Security teams must have access to numerous data sets containing malicious codes, malware codes, and anomalies. Some businesses just lack the time and resources to gather all of these precise data types.
Hackers also employ AI: they refine and enhance their malware to make it immune to AI-based protection measures. Hackers can create more sophisticated assaults and target conventional security systems or even AI-boosted systems by learning from already-existing AI tools.
Putting the ‘I’ in ‘AI’: Unfortunately we’re still at a stage where machine learning systems still require some form of human oversight and input. It’s why we have far reaching issues with bias inside AI systems and it’s highly likely delegating a company’s entire security posture to a machine learning process would cause more headaches than it would ease.
In conclusion (while enhancing security) artificial intelligence and machine learning can make it simpler for hackers to break into networks without human assistance. This has the potential to seriously harm any business. If you want to minimize damages and maintain your business, striking some sort of balance between AI and real human cyber security experts is strongly advised.