Projections show cybersecurity spending exceeding $133 billion by 2022, including spending on artificial intelligence and machine learning solutions. Many businesses use AI to assist in breach detection and prevention, but as the technology becomes more ubiquitous, hackers are turning the tables and deploying AI-powered attacks. If such sophisticated solutions can backfire, can enterprises really rely on AI for their security needs while mitigating artificial intelligence threats and security issues?
A Few AI Statistics
According to Gartner, information security and risk management spending could be as much as $175.5 billion by 2023. Seventy-five percent of enterprises currently rely on AI-based solutions for network security, and 51% use AI as a “primary” threat detection option.
These numbers suggest increasing confidence in sophisticated cybersecurity solutions, but 22% of organizations still lack sufficient resources to respond when incidents occur. There remains a significant gap between the 62% of enterprises making the most of AI and exploring new ways to implement AI solutions and those with little or no solid grasp of how to properly implement the technology.
AI is Changing Cybersecurity (For Better or Worse)
Speed is where AI excels the most by surpassing the human capacity to detect and mitigate threats. Seventy-five percent of cybersecurity executives agree AI allows them to respond to breaches faster, and the technology has been found to speed up evaluations of “breach-worthy” vulnerabilities by 73%. Fifty-nine percent of cybersecurity professionals say AI streamlines the process of detecting and responding critical system weaknesses, and enterprises using the technology are able to find and fix such weaknesses 40% faster.
What does this mean for enterprise cybersecurity in practice?
With the rapidly evolving threat landscape, AI has become a necessity for 69% of enterprise executives. Sixty percent of cybersecurity professionals agree the technology is able to provide networks with “deeper security,” which can be a critical factor in separating enterprises affected by breaches from those able to avoid attacks.
Artificial intelligence shows significant potential for detecting fraudulent activity, malware and intrusions, as well as gauging the risk levels of login attempts. By making threat detection more sensitive and enabling nuanced behavior tracking, AI increases flexibility within identity and access management strategies. IT professionals can use the technology to create conditional rules and reduce friction for users with complex access requirements.
AI Can Backfire in the Hands of Hackers
Ironically, speed is also a major drawback of AI. Hackers are embracing the machine learning algorithms behind the technology’s success to create nuanced attacks personalized for specific individuals. Because AI can be “taught” with data sets, hackers can either create their own programs or manipulate existing systems for malicious purposes. Attacks executed with AI tend to be more successful, perhaps because the technology makes it easier to develop malware with the ability to evade even sophisticated threat detection. For example, pairing polymorphic malware with AI allows these programs to change their code rapidly, making them almost invulnerable to existing cybersecurity systems.
Hackers may also modify enterprise machine learning algorithms by altering inputs to change the way the system recognizes specific elements. This technique can be used to make the system overlook threats and allow hackers to bypass identity and access management controls.
System behaviors are potential targets, as well; with the right modifications, hackers can change the way devices respond or communicate, which may result in dangerous outcomes. Once system information has been changed, it can be very difficult to correct problems and return the network to its original state.
In light of these threats, it’s important for enterprise executives and IT professionals to resist the temptation to be complacent. Although AI is becoming more autonomous, it is by no means a replacement for human diligence. Systems require correct setup and management from the start, beginning with extensive data sets to prevent false positives and continuing with consistent monitoring and updates to maintain strong security.
Avoiding the Pitfalls of AI Technology
No single security solution, including AI, is enough to protect enterprise networks on its own. In addition to developing robust cybersecurity policies for comprehensive protection, enterprises must:
• Promote cybersecurity awareness through ongoing employee education
• Prioritize data protection
• Employ IT professionals with an awareness and understanding of emerging threats
• Use high-quality data sets when training AI systems
• Automate key security processes for faster detection and response
• Go beyond compliance to create tailored security solutions
• Perform routine security audits and penetration testing
• Upgrade software and hardware as needed
• Amend security policies to address new threats
Like all security solutions, artificial intelligence has its limitations. Enterprises interested in incorporating the technology into cybersecurity frameworks must assess their needs and design multifaceted strategies to address both known and potential threats. Instead of seeing AI as the ultimate solution to all cybersecurity problems, it’s necessary to acknowledge potential drawbacks and implement the technology as part of a dynamic and adaptable security solution.