Incorporating solutions and strategies to identify the threat areas in cybersecurity networks, helps minimize the risks associated with using AI should remain the top priority for business success.
Any technology is a double-edged sword with its own pros and cons. And while pros are imperative in the organization’s governance, the cons reflect the flaws of technology, which is difficult to ignore. Hence AI is no exception for being a threat, especially considering cybersecurity.
Over the years, the threat concerning AI for cybersecurity has grown. Despite AI being for facial recognition and to enhance the business environment, its risks are undeniable. There are also opinions that advanced AI has provided entry points for cybercriminals to launch attacks on businesses, organizations, and political institutes. Even social media platforms are now at the frontline for being abused by malware attacks.
In 2019, the Israeli Spyware Pegasus raised a sudden alarm about the use of Artificial Intelligence in trespassing cybersecurity. It exposed the otherwise most underestimated misuse of AI for privacy violations.
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However, there are many other players as well who have pointed out the flaws of AI in cybersecurity. The manipulation of political systems, discrimination, threats to national security, and misuse of customer data are amongst the top listed threats of AI that shake the current cybersecurity landscape.
Hence, firms need to be more vigilant in understanding the existing security threats with AI, in augmenting the data processing and effectively identifying the necessary measures required to implement AI using cybersecurity.
In October 2019, The World Economic Forum has listed cyber-attacks amongst the ten global risks to be considered immediately. It is estimated that businesses worth approximately $US 90 trillion can be lost in a decade’s time if timely actions are not taken to combat the burgeoning cyber-attacks.
Humongous amounts of complex data are received by different social media, web portals, mobile devices, sensors, and IoT. It becomes challenging to identify data malware, which often shows up as anonymous data.
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Often security creates hurdles within a company act like a loophole through which data can get discriminated by weaving zip code – infringing the user’s privacy.
Hence it is imperative for any organizational set up to precisely identify the areas that require utmost attention – strategizing a solution to recognize the absent risk management system, and fix the threat incorporated within the cybersecurity framework.
This can be accomplished by automating cybersecurity with AI to facilitate certain decisions in cyberspace. The threats with the cyberspace must be listed, categorized, and rated. This segregates the vulnerable points in the cybersecurity network, which requires an immediate strategic solution to block various cyber-attacks.
Incorporating an AI-driven cybersecurity model to augment current cyber capabilities also helps in analyzing the existing potential threat within the network. It advances the efficacy and quality of the cybersecurity operations and recognizes the abnormal patterns in operations. It also enhances detection and response mechanism, reforms the classification of data, prioritizes vulnerabilities, and ensures effective Deepfakes detection and analysis.
