As the incidents of cyberattacks are constantly rising- both in terms of volume and scope, it is clear that the conventional defense mechanism isn’t fully capable to fight latest AI-powered cyber threats. To effectively fight these sophisticated threats we need an equally advanced defense system. We can achieve this through AI powered security that can offer much better protection against attacks predicts even the hidden risks and prompts actions to foil the attack before it triggers. In short we need to use Good AI to defend against Bad AI. In this article we will discuss different ways in which AI can help in fighting cybercrimes and staying one step ahead of threat actors:

How is Bad AI helping threat actors?

Let us start with understanding the different ways in which malicious elements are using AI to increase the volume and impact of attacks. It will allow us to understand the limitations of conventional systems and the need for AI enabled security:  

Scaling up volume of attacks

By employing data science and machine learning threat actors can easily scale up the volume and scope of attacks like Spear phishing which involves social engineering and usually consumes lots of time. Along with collecting huge volumes of targets’ data the attackers also need to analyze their demographic aspects and compose contextual mails to gain trust. Through ML and data science attackers can not only extract huge data but also compose authentic sounding mails and send them to intended recipients.

Deepfake

Deepfake is fast emerging as one of the most serious AI cyber threat that can be employed for influencing human psychology and emotions to spread disinformation, and distrust, or deceit the audiences. More advanced Deepfake can compromise business emails by impersonating as trusted contacts to financially defraud the companies? Deepfake can even mimic the human voice making it possible for attackers to use more trusted platforms like audio or even video evidences to gain unauthorized access or psychologically influence the victim prompting them to specific actions like transferring money, or sharing hypersensitive information.

Misleading the detection tools

Data poisoning focuses on weakening the defense mechanism at its core by corrupting the training data of advanced threat detection tools. In this attack the threat actors inject malicious and misleading data into training data that may cause the tool to, say, identifying a spam looking mail as a safe mail and send it to primary folder of inbox.

Continuous evolution

Unlike the conventional attacks the AI attacks can automatically- and continuously evolve based on experiences which means the power and scale of AI attacks keeps on increasing with time- thanks to machine learning. Besides, automation also helps the attackers to increase both frequency and scope of attack thus increasing the damage.

How can AI help in cybersecurity?

In the above sections you read about various ways in which AI is being misused by threat actors to conduct malicious activities. Such sophisticated attacks can easily evade the conventional security system. However, by using AI powered security, we can fortify the defense mechanism, making it powerful enough to fight against the latest threats: 

Advanced threat detection through Machine learning

Using advanced AI technologies the threat actors can employ sophisticated techniques that conventional firewalls and antimalware systems aren’t capable to detect or fight. Machine Learning makes defensive system smarter by monitoring and detecting anomalies in network usage patterns among staff and promptly alerting supervisors upon noticing any suspicious pattern.

With threat actors constantly improving their attack system through AI, we need equally effective AI-enabled security that can automate monitoring, detection and prevention to foil these attacks.

AI-enabled authentication 

Authentication is the key gateway for security and thus the main target for threat actors. Through Deepfake AI, attackers can use context based access requests to invade the endpoints and inject malicious elements. It enables them to weaken even advanced access methods like biometric authentication. 

To defend against that you can employ Risk-Based Authentication tools that can detect any anomalous activity through AI-powered behavioral biometrics deny the access if required. RBA, which is also called adaptive intelligence, is even more advanced technology that can assess various details like IP address, location, data sensitivity, and device information to measure risk score and accordingly permit/restrict the access. In that capability it helps in defending against real time attacks.

For example if a person who generally logs in during weekdays through office desktop, tries to log in from a business center through Smartphone on weekend, then system will promptly flag it as suspicious activity.

AI-powered authentication systems not only secure the authentication during entry but also work in the background to constantly analyzing the user behavior throughout the session to safeguard against midway attacks. So, if an authorized person leaves the seat without logging out, and someone tries to tamper with it, the RBA security model can issue warning upon noticing any difference in usage pattern (like altering sensitive settings, etc.).

Foiling phishing attacks with AI

 Deepfake is one of the most widely used AI technologies used in modern phishing attacks. For instance, attackers may send you can email impersonating as your manager, asking to review a document. In such cases, the defensive AI tools can deeply scan the features like writing flow, word choice, grammar and syntax to spot suspicious variance from the regular manner of the sender’s communication style.  

 AI can also detect email address and altered signature by reviewing email metadata. For additionally security AI can also verify the authenticity of links and images. In that capability, the defensive AI can help in defending against social engineering something that conventional malware aren’t capable to detect. So, for AI powered phishing mails that can easily bypass conventional filters, the defensive AI employs 360-degree surveillance to assure maximum security. 

Social engineering attacks influence the psychology of recipients to gain their trust or prompting them to take hasty actions. AI can identify the email patterns that human eye may not notice. An employee engaged in some work may easily miss verifying authenticity of the email but AI-powered monitoring tools can instantly detect any red flags and takes preventive action. 

Forecasting potential attacks for proactive measures

AI can help in building proactive defenses against upcoming/possible attacks to foil them before they are triggered. AI enables supervisors to gain complete visibility over the entire network infrastructure. By automatically flagging any vulnerability, AI empowers them to keeping all the endpoints secure- something that is not manually feasible in the present times with work from home and BYOD policies.