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AI has transformed cyberthreats: 80% of ransomware attacks examined in new MIT research used artificial intelligence, from deepfakes to AI-generated phishing campaigns.Three pillars for AI defense: Effective defense combines automated security hygiene, autonomous defensive systems, and augmented executive oversight with real-time intelligence.+++ <\/p>\n
New research from Cybersecurity at MIT Sloan<\/a> and Safe Security<\/a> examined 2,800 ransomware attacks and found that 80% of them were powered by artificial intelligence. AI is being used to create malware, phishing campaigns, and deepfake-driven<\/a> social engineering, such as fake customer service calls. Large language models are being employed to generate code and phishing content. There is also AI-enabled password cracking, CAPTCHA bypass, and more.<\/p>\n Perhaps you\u2019re thinking that the answer is to fight fire with fire by building AI-powered defenses. But that\u2019s only part of what\u2019s needed, according to the researchers.<\/p>\n \u201cAI-powered cybersecurity tools alone will not suffice,\u201d they write. \u201cA proactive, multi-layered approach \u2014 integrating human oversight, governance frameworks, AI-driven threat simulations, and real-time intelligence sharing \u2014 is critical.\u201d<\/p>\n <\/p>\n \n Rethinking the Cybersecurity Arms Race\n <\/p>\n Read the Report [PDF] The 3 AI defense pillars every organization needs<\/p>\n The researchers argue that a comprehensive approach to combating AI-enabled threats consists of three types of defense, all of which are essential:<\/p>\n Automated security hygiene, such as self-healing software code, self-patching systems, continuous attack surface management, zero-trust-based architecture, and self-driving trustworthy networks. Automating these routine tasks reduces manual workloads while strengthening protection against attacks that target core system vulnerabilities.Autonomous and deceptive defense systems, which use analytics, machine learning, and real-time data collection to learn from, identify, and counteract threats. Examples include simultaneously automated moving-target defense, and deceptive tactics and information. Both types of systems enable teams to take a proactive approach to defense, rather than getting stuck in reactive mode.Augmented oversight and reporting, which give executives real-time data-driven insights. For example, automated risk analysis uses AI to spot emerging threats and predict how they might impact an organization.What this means for cybersecurity today<\/p>\n Cybersecurity professionals should look at the history of successful cyberdefenses \u2014 protections against phishing, social engineering, and malware attacks, for example \u2014 and consider how familiar forms of attack could evolve with the addition of AI.\u00a0<\/p>\n At the MIT Computer Science and Artificial Intelligence Laboratory, for example, researchers have developed a method of defense called artificial adversarial intelligence<\/a>, which mimics attackers to test network defenses before real attacks happen.\u00a0<\/p>\n \u200b\u200b\u201cThe autonomous nature of things has caused there to be a reexamination of the way in which we defend ourselves and the way in which we have to look at both old- and new-style attacks,\u201d said Michael Siegel<\/a>, the principal research scientist and director at CAMS, and an author of the report.\u00a0<\/p>\n But it\u2019s yet to be seen how the eternal game of whack-a-mole for security teams will change with AI both deployed regularly in attacks and employed in defense.\u00a0<\/p>\n \u201cCan we crack the asymmetric warfare nature of cybersecurity?\u201d Siegel asked. \u201cRemember that the attacker only needs one point of entry and exploitation while the defender must stop all entry points and be resilient to all exploitations.<\/p>\n \u201cFor cybersecurity, there are tremendous opportunities for things to go wrong,\u201d he said. \u201cProtecting in this new environment that is moving at light speed is challenging, but we can learn from our previous work. Many researchers and products are already addressing management, prevention, detection, response, and resilience issues.\u201d<\/p>\n One example of that work: Siegel and colleagues from MIT Sloan are investigating the role generative AI is playing in both attacks on and the defense of industrial control systems<\/a>.\u00a0\u00a0<\/p>\n Read the full report<\/p>\n
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