APN News

  • Wednesday, September, 2022| Today's Market | Current Time: 08:06:39
  • By Derek Manky, Chief of Security Insights and Global Threat Alliances at FortiGuard Labs.

    Artificial intelligence (AI) technology is a powerful technology, and because of this, it holds great potential for exploitation by cybercriminals. Considering this, the only way that security leaders can stay ahead of bad actors is by gaining a true understanding of how this technology can be weaponized. Then, they can begin to develop effective strategies for confronting AI threats head-on.

    Malicious Uses of AI Technology

    As AI grows in adoption and sophistication, cybercriminals are looking for ways to seize upon its potential. The Electronic Frontier Foundation was already warning about potential malicious uses of AI back in 2018, including threats to digital, physical, and political security. And now, AI precursors combined with swarm technology can be used to infiltrate a network and steal data. 

    Hacking into a network used to take months. But with AI and machine learning (ML) technologies on their side, cybercriminals can see this time span reduced to a matter of days. As more AI-enhanced attacks are orchestrated, the techniques used in these events become increasingly available and inexpensive for more and more cybercriminals. 

    Automated and scripted techniques can also exponentially increase the speed and scale of a cyberattack. The ability to automate the entire process of mapping networks, discovering targets, finding vulnerabilities, and launching a custom attack significantly increases the volume of attacks even a single bad actor can pull off.

    Complex Networks Often Lack a Cohesive Security Strategy

    Often, network security architectures are not designed to stand up to these types of attacks. For example, it’s not uncommon for an organization to use 30 or more security-related point products within their environments. With such a setup, getting a big picture view of the organization’s security architecture requires manual consolidation of data across the different applications.

    This also leaves such organizations unable to quickly launch an effective coordinated response to a network-wide attack. And as cybercriminals continue to minimize their exploit times, IT security teams are left struggling to detect attacks at the same speed. In fact, the 2020 Ponemon Cost of a Data Breach Report notes that the average breach detection gap (BDG), which is the time between the initial breach of a network and its discovery, is 280 days. The report also found that the average cost of a data breach in the United States is $8.64M, 124% higher than the global average ($3.86M). Considering this, it is more crucial than ever that organizations adopt new strategies to make sure their networks can function as cohesively as possible. 


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