Why Automation Is Critical to Fight Social Engineering Attacks

AI and machine learning are invaluable for detecting, analyzing, and blocking malicious content online. Individuals and organizations alike can use these tools to stop social engineering schemes in their tracks. How can users apply automation to fight social engineering in an ever-growing threat landscape? Why should users consider adopting automated security tools?

How AI and Automation Can Fight Social Engineering

Developers are applying AI, machine learning, and other automation technologies in nearly every niche today. Why are these tools particularly well-suited for fighting social engineering? Automation offers the heightened awareness and flexibility necessary to stay ahead of social engineering schemes.

AI-Generated Training Materials

Training is a crucial part of warding off social engineering attacks. When people know how to identify social engineering content confidently, they are far less likely to fall for scammers’ tricks. It can be difficult to find good examples of phishing content for training safely, though.

This is a great opportunity to use AI and automate the writing of social engineering training programs. Hackers are using generative AI to create more realistic phishing messages, but security pros can use that tool against them. AI can generate realistic but fake emails that reflect the type of content phishing schemes scammers are using today.

Trainees can use this material to spot increasingly subtle red flags that indicate potential social engineering content. Generating fake phishing emails also ensures training leaders don’t need to go looking for real phishing content, as well.

AI Phishing Detection

Social engineering is getting harder to detect now that hackers can access tools like generative AI. Luckily, AI and machine learning can also automate spotting generated content.

Phishing detection has become one of the top applications for AI in cybersecurity. Machine learning allows AI phishing detection to adapt rather than relying on static rulesets for identifying malicious content. This flexibility makes AI much more effective than conventional tools since it can keep up with changing attack strategies.

Since more and more social engineering content will be AI-generated moving forward, people can identify it using AI content detection tools. These automated platforms analyze input text for signatures of generative AI, such as underlying patterns or speech quirks.

Users can copy suspicious text into an AI content detection tool. If the text is AI-generated, it’s more likely to be social engineering content. Several free AI-generated content detection algorithms are available online, including one created by OpenAI — the developer of ChatGPT.

Automated Phishing Filtering

Email is one of hackers' most common channels to deliver social engineering content. Users can fend off these attacks using automated phishing filters.

Spam filters have long been part of most mainstream email services, but AI is making these filters smarter and more effective. It’s also bringing phishing filtering to other channels, as well, such as web browsing and downloads. For example, most modern antivirus programs include automated download analysis, which can detect hidden malware.

Security pros can even connect phishing filtering systems to threat monitoring tools for analysis. The filter can automatically transfer identified phishing content to threat intelligence so the content can get thoroughly parsed for risks and clues. The threat intelligence analysis can highlight malicious email addresses, improve filtering accuracy, and identify any key risks in reported content.

Threat Monitoring

Automated threat monitoring is invaluable for defending against social engineering attacks. According to the NIST, there is a shortage of 3.4 million cybersecurity professionals worldwide. With fewer people available to analyze potential social engineering content manually, automation is vital for keeping up.

Automated threat monitoring can take a variety of forms. For instance, an AI threat detection extension could run in the background on a browser, autonomously scanning web pages for phishing content. Likewise, automated analysis tools can analyze known phishing content for valuable threat intelligence.

Connecting automated phishing filtering to a larger security intelligence program can reveal trends in the types of social engineering attacks someone is receiving. By monitoring this data over time, a user might be able to tell if their data was exposed somewhere and — if so — what data is at risk.

Why Adopting Anti-Phishing Automation Is Crucial

Automation can fight social engineering attacks in several ways, but why should users adopt this technology? It’s vital today due to the ever-increasing volume of social engineering content online.

In the first quarter of 2022 alone, there were over 1 million phishing attacks. In addition to the sheer number of these attacks, the threat landscape is also growing and evolving, with attacks becoming harder to detect. Individuals and organizations face challenges keeping up with modern social engineering. There are simply too many potential attack vectors and too many subtle red flags for most users to analyze manually.

AI, machine learning, and other automation tools allow users to stay one step ahead of hackers. In fact, many emerging types of phishing attacks may only be reliably detected using AI tools.

For example, clone phishing copies a real email precisely except for a minor change, such as altering a link to send users to a malware website. Many users would understandably fall for the trick and click the malicious link. AI can autonomously scan all of the content in the email, spot the fake link, and flag the email as phishing, all within seconds.

Similarly, hackers are creating entire websites for social engineering. They copy an actual web page exactly but alter the URL slightly. For example, the hacker might change apple.com to app1e.com.

Many users won’t notice such a minor change and may give away login credentials or payment information on the fake website. Automated phishing and spam detection tools constantly scan pages in the background, even before the page loads. These tools would notice the fake URL right away and block the page.

Automation gives users a cybersecurity sixth sense that makes social engineering detection effortless. Unfortunately, most people assume a website or message is trustworthy until they’ve already fallen victim to a social engineering scheme. Additionally, many people find it frustrating to analyze every website, email, and message they see online.

Automation takes over the “hassle” task of analyzing all that content. Users can even use automated tools to analyze social engineering detection data so they can get valuable insights about the unique risks they’re facing, further improving security.

Defending Against Social Engineering

Automation is today’s best defense against social engineering. AI and machine learning can autonomously generate phishing training materials, detect malicious content, filter out phishing messages, and monitor security data.

Social engineering content is only getting more common and harder to detect. Automated tools make detection seamless, easy, and effective. Machine learning allows automation to adapt as social engineering tactics change. With these boons, anyone can ward off social engineering cyber attacks.