The Major AI Threats Cyber Security Teams Must Deal With

AI tools can significantly strengthen cyber security tools makig network security, anti-malware, and fraud-detection software more powerful by detecting suspicious activity much faster than humans can.

Right now,  AI presents major opportunities in cyber security, including the ability to defeat cyber attacks at machine speed, advance threat intelligence, and close the skills gap in the cyber security workforce. But it is a double edged sword.  Both defenders and attackers can use the same tools and AI also presents a significant risk to cyber security.

Brute force, denial of service (DoS), and social engineering attacks are just some examples of threats that make use of AI and as cyber security operates as an asymmetrical game, and the risks of AI to cyber security are expected to increase rapidly with AI tools becoming cheaper and more accessible. 

Prompted by the release of ChatGPT,  AI has captured the world's interest and has the potential to bring many benefits to society and as the digital landscape evolves, so do the electronic threats. Cyber security, which used to be just a partial threat concern, is now at the forefront of global security discussions. AI is a game-changer for cyber security and while AI offers robust defensive capabilities, it also equips cyber attackers with powerful tools to enhance their malicious activities. 

The Big Five  AI Threats

Automated Phishing Attacks:  Phishing remains one of the most prevalent forms of cyber attack. Traditionally, phishing relies on large volumes of emails sent indiscriminately, hoping a few targets will take the bait. 
However, AI has transformed this spray-and-pray approach into a precision-guided missile. AI algorithms can analyse social media profiles, public databases, and previous communication patterns to craft highly personalised and convincing phishing messages. 

Imagine receiving a message that knows the victim’s professional history but also his or her recent vacation details and personal interests, creating a highly believable narrative.

For example, a corporate executive might receive an email appearing to be from a trusted colleague, referencing recent projects or personal details gleaned from social media. These sophisticated spear-phishing attacks are designed to bypass common security measures and exploit human trust. With AI, the volume and accuracy of these attacks can increase dramatically, making traditional detection methods less effective. 

AI-powered malware:  Malware development has been revolutionised by AI. AI-driven malware can adapt its behaviour based on the environment it infects, making it more difficult to detect and eradicate. 
This includes polymorphic malware that constantly changes its code to evade traditional signature-based detection methods. 

Consider an AI-powered ransomware that modifies its encryption algorithms and communication patterns based on the defences it encounters within a network.

This adaptability lets it remain hidden and effective for longer periods, increasing the potential damage. Furthermore, attacker can use AI to automate the creation of malware, enabling the rapid development of new variants designed to exploit specific vulnerabilities, significantly reducing the window of opportunity for defenders to respond. An example is Emotet, a polymorphic malware that has evolved to evade detection by changing its code frequently and using AI to identify the best targets within a network.

Deepfakes:  Deepfakes use AI to create highly realistic, yet fake images, videos, and audio. Cyber criminals can use deepfake techniques to impersonate individuals, creating fraudulent communications that can deceive even the most discerning recipients. Imagine a scenario where an executive receives a video call from what appears to be their CEO, instructing them to transfer funds or share sensitive information. 

The realism of deepfakes makes it very challenging to distinguish between legitimate and fraudulent communications.

Attackers can leverage this technology for social engineering attacks, corporate espionage, and even to manipulate stock prices by spreading false information through seemingly credible sources. Convincing deepfake video of a political figure could destabilise markets or incite public unrest by spreading misinformation.

AI-driven Reconnaissance:  AI can also enhance the reconnaissance phase of cyberattacks. Attackers can use AI to sift through massive amounts of data, identifying potential vulnerabilities and targets with greater speed and accuracy. For example, an AI system could scan an organisation's network, analysing traffic patterns, user behaviour and system configurations to identify weaknesses. 

This level of automated reconnaissance lets attackers plan and execute their attacks with unprecedented precision, targeting specific systems or individuals who are most likely to yield valuable information or access. 

AI-driven tools can search for vulnerable devices connected to the internet, offering attackers a roadmap of exploitable targets.

Autonomous Weapons & DDoS attacks:  AI-powered autonomous systems can be employed to conduct Distributed-Denial-of-Service (DDoS) attacks. These systems can independently locate and exploit vulnerable devices to create botnets, which can then launch massive DDOS attacks capable of overwhelming even the most robust defences.

The integration of AI makes these attacks more resilient and difficult to mitigate. For example, an AI-driven botnet could dynamically adjust its attack patterns based on the responses of the targeted systems, effectively learning in real-time to maximise disruption. 

This level of sophistication requires equally advanced defensive measures to counteract. The Mirai botnet, which was used in a massive DDoS attack in 2016, exemplifies how attackers can harness autonomous systems to exploit vulnerable IoT devices and launch large-scale attacks.

The Growing Cyber Security Challenge

The integration of AI into cyber attack strategies real e challenge for security leaders. Traditional methods of defence are becoming increasingly obsolete as attackers use AI to overcome them.

The dynamic nature of AI-driven threats requires a paradigm shift in how we approach cyber security.

By staying informed and prepared, we can better defend against the ever-evolving landscape of cyber threats.
And in recent years, AI has emerged as required technology for augmenting the efforts of human information security teams.

Since humans are no longer capable to adequately protect the enterprise attack surface, AI provides much needed analysis and threat identification that can be acted upon by cybersecurity professionals to reduce breach risk and improve security posture.

NSCS   |   Gov.UK   |   Malwarebytes   |   SC Magazine   |    Microsoft   |   Balbix  

Image: Black_Kira

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