The types of cyberattacks bad actors gravitate to haven’t changed – only their sophistication. Phishing attacks are more popular than they’ve ever been. Chiefly, because of how accessible they’ve become through AI and machine learning.
Staying ahead of modern-day cyber attacks will require organizations to fight fire with fire. This means adopting an AI-driven strategy to counteract high-level threats.
The evolving threat of phishing attacks
In 2024, the estimated average global cost of a data breach was 4.88 million dollars – a 10% increase over the previous year. At least 36% of all data breaches were due to a phishing campaign.
It’s easy to see why phishing campaigns remain so popular. They’re simple and effective. All one needs is a solid grasp of English, decent web design skills, a bit of SEO trickery and poof—unsuspecting victims will be handing their SSN over without thinking twice.
Hence, threat actors seldom use them as an end, but rather to inject systems with ransomware and/or extract data. And they’re very effective, too, with 94% of organizations reporting email security incidents, in large part due to successful phishing attempts.
Unfortunately, generative AI only made things worse. It’s a stark reminder that while the democratization of AI has been a boon in many aspects, it’s also substantially shifted the landscape of cybersecurity. Just remember what happened to that Ferrari executive, who was one personal question away from unwittingly siphoning the Italian car manufacturer’s funds to a scammer.
Understanding AI-powered phishing attacks
While Ransomware-as-a-Service has been a blessing for script kiddies, bad actors don’t have to go as far as the dark web to create a successful phishing campaign.
To use an example, the infamous Yahoo Boys use generative AI, deepfakes, and real-time face and voice-swapping software to carry out convincing pig butchering scams. Bad actors can also use these tools to mimic peers or superiors to carry out a whale phishing expedition.
A generative text AI tool could allow a threat actor to craft convincing emails that appear to be from the IT department. They can then persuade a user to grant them remote access to their computer.
Upon remote entry, they can plant malware or ransomware. If the user has high access privileges, they can leverage the computer as a launch pad to obtain business analytics, employee records, or worst of all—confidential IP. They can even move laterally through the organization’s network infrastructure and systems.
This combination of a phishing campaign and social engineering can have disastrous consequences. In fact, not so long ago, Russian hackers used a similar exploit to access the computers of Microsoft Windows users, and subsequently spread Black Basta ransomware.
Flipping the switch: Using AI for protection against phishing
The true value of machine learning in this context is in its ability to rapidly amass and process information. Machine learning algorithms can sift through vast amounts of data to identify patterns and anomalies and can protect against phishing just as well, if not better than they can proliferate such attacks. This includes several approaches, such as:
- Deep learning techniques such as Convolutional Neural Networks (CNN) and Long-Short-Term Networks (LSTN) are highly effective phishing pattern recognition tools. IT security teams can utilize them to create continuously updated phishing domain lists. The system automatically blocks or removes any message from one of the domains on the blocklist.
- Likewise, Text-CNN algorithms are available as programming libraries, making it easier for developers and administrators to incorporate them into email servers and clients as well as develop powerful anti-spam filters.
- Natural Language Processing (NLP) can further enhance anti-phishing by scanning messages for suspicious language and patterns. Some of these NLP techniques are used in AI-generated text detection tools and can be reverse-engineered to uncover GPT-isms in cleverly AI-generated emails sent with the purpose of phishing.
- Another way to automatically analyze and compare user behavior at depth is through machine learning-powered software. If the software encounters any anomalies, it notifies the relevant parties and/or launches automated security measures. This can include revoking user access, disconnecting the user computers from the network, or running anti-malware software.
Proactive defense strategies for businesses
Advanced cybersecurity platforms have an integral role in addressing emerging threats. However, we should not use these tools to fill in the cracks. Businesses should strive to improve their entire security posture. If this means redoing your entire approach or philosophy, so be it.
For instance, more software development firms and teams have begun incorporating security-first development principles. By doing this, they can take a bulk of app security out of the hands of the users.
Most of the time, despite all the users’ faults, the burden of responsibility falls upon the developer—whether they use the right tools to test their app, are their software partners compliant with relevant security postulates, and what proactive steps are they taking to prevent phishing attacks.
Software companies aren’t the only ones that can benefit from this approach. All businesses with significant IT infrastructure can enhance their cybersecurity readiness by embracing Zero-Trust architecture.
This involves retraining staff, incorporating continuous authentication and authorization, limiting user access to resources, and adding multi-factor authentication. You’ll find that many machine learning and AI tools can facilitate many of these techniques.
Implementing effective security measures
Phishing and social engineering attacks primarily aim to exploit the insider threat. Hence, the importance of encouraging vigilance by regularly educating staff. Establishing a culture of cybersecurity awareness can help prevent phishing attacks.
There are no one-size-fits-all solutions for cybersecurity. Like how bad actors tailor attacks to specific targets, you should tailor your anti-phishing security strategy to the circumstances of your company.
Consider collaborating with a reputable third-party cybersecurity expert knowledgeable in Zero-Trust practices, phishing attacks, and utilizing machine learning to counteract them. But where should you start when building your anti-phishing cybersecurity strategy?
Adding a robust security and event management (SIEM) solution to your security stack is also a good place to start. They allow organizations to monitor, process, and analyze security-related data. Many of these tools are AI-powered. This makes them ideal for organizations looking to incorporate machine learning but don’t know where to start.
Coordination, collaboration, and communication
Organizations can reveal vulnerabilities and improve overall security by performing regular risk assessments and security audits.
We can learn a lot from how cybercriminals coordinate and organize attacks. Groups, cybersecurity experts, and organizations must collaborate in a similar fashion to address phishing campaigns and other cyber-attacks. Information can be shared between entities using secure peer-to-peer systems.
America’s National Cyber Defense Agency (CISA) has launched and implemented a variety of information-sharing programs that organizations and individuals can join. You can then use this information to review and update your security practices and policies – which should be done regularly.
AI-enhanced phishing attacks will remain a significant threat to businesses
In the future, we can see quantum computing and advancements in GPU architecture fortifying AI. While AI is currently under-regulated, this may change in the future as governments catch up. Until then, AI-enhanced phishing attacks will remain a significant threat to businesses.
Fortunately, it’s possible to even the playing field by incorporating predictive analytics and machine learning into your cybersecurity stack.