In this Help Net Security interview, Carl Froggett, CIO at Deep Instinct, discusses emerging trends in ransomware attacks, emphasizing the need for businesses to use advanced AI technologies, such as deep learning, for prevention rather than just detection and response.
He also talks about the shift in budget priorities in 2024 toward ransomware prevention technologies.
Recent data from Deep Instinct found that the total number of ransomware victims in 2023 increased significantly.
Amazingly, there were more victims of ransomware attacks in the first half of 2023 than in all of 2022.
This clearly indicates to me that what we currently have as an industry is failing and, once again, we need a shift to combat the evolving threat landscape.
The attacker techniques have changed; ransomware attacks are being carried out as large-scale campaigns, affecting a significant number of victims at once, such as what we saw this year with the Zimbra and MOVEit vulnerability attacks.
Thanks to the advanced capabilities of AI, we can now prevent ransomware and other cyber attacks rather than just detect and respond to them.
Responding is no longer good enough as the evidence shows, we need to go back to a prevention first philosophy, with prevention capabilities embedded at different points in infrastructure, storage and business applications using AI. This is the only way businesses can truly protect themselves from advanced forms of ransomware and threats, specifically by leveraging a more sophisticated form of AI to fight against AI threats - such as deep learning.
That's especially evident when you compare deep learning versus machine learning-based solutions.
Most cybersecurity tools leverage Machine Learning models that present several shortcomings to security teams when it comes to the prevention of threats.
These offerings are trained on limited subsets of available data, offer just 50-70% accuracy with unknown threats, and introduce many false positives.
Because DL models understand the building blocks of malicious files, DL makes it possible to implement and deploy a predictive prevention-based security program - one that can predict future malicious behaviors, detecting and preventing unknown threats, ransomware, and zero-days.
Security operation center teams are inundated with alerts and potential security threats they need to investigate.
They produce extremely low false positive rates because they're very accurate, giving SOC teams back time to focus on real, actionable alerts and pinpoint threats faster, with greater efficiency.
By spending time on real threats, they can optimize their threat posture and engage in more proactive threat hunting which significantly improves the risk posture of their organization.
With 62% of the C-suite confirming ransomware was their number one concern this past year, we'll see businesses shifting their budgets in 2024 - investing in prevention technologies that stop ransomware, known and unknown threats, and other malware.
Clearly, given the evidence, this approach is failing rapidly year on year due to the threat landscape changing.
Security teams won't win the battle against AI with legacy tools; rather, organizations require cybersecurity solutions that are natively built with DL models to mitigate the volume and velocity of evolving AI threats.
In 2024, we'll see organizations make room in their budgets to integrate advanced AI technologies into their cybersecurity strategies to enhance security resilience and mitigate the likelihood of successful attacks.
In 2023, we saw AI burst onto the scene; 2024 will be the year AI becomes part of business planning, processes, and decision-making.
This Cyber News was published on www.helpnetsecurity.com. Publication date: Tue, 05 Dec 2023 05:43:05 +0000