AI Cyber Attacks: Characteristics, and Best Practices for Prevention

How Can AI Be Used in A Cyber Attack?

These algorithms automate tasks such as vulnerability scanning, campaign deployment along targeted attack vectors, lateral movement within networks, and the establishment of persistent backdoors.

Target Reconnaissance

AI automates target identification by scraping public sources, such as social media and corporate sites, in order to gather intelligence. From there, it’s possible to gain an understanding of:

• Network topologies
• Employee roles
• Exposed assets

…streamlining and enhancing the precision of the recon process.

Fuzzing

Fuzzing is a software testing technique that feeds random, malformed, or unexpected inputs into a program to discover bugs, crashes, or security vulnerabilities. AI models, especially reinforcement learning and generative models, can learn how a target program responds to different inputs.

Over time, they prioritize fuzzing inputs that are more likely to trigger:

• Crashes
• Unexpected behavior
• Access control violations

This increases the likelihood of uncovering vulnerabilities faster than brute-force fuzzing.

Phishing

Deepfake technology represents an additional frontier for phishing attacks: it allows attacker-controlled models to intake videos and .wav files and recreate them into synthetic audio, video, or imagery.

As a result, employees can be duped into sending attackers money or downloading malicious files. Plus, some employees are more vulnerable to phishing attempts than others: those in public-facing positions who handle an organization’s financial matters are particularly common victims.

AI can automatically ingest corporate social media info – from LinkedIn, for instance – and provide attackers with a full list of possible victims. This can also be cross-referenced against contact details and personal information included in other social media sites, or even leaked credentials.

AI phishing methods have been employed for over half a decade at this point; one of the more recent high-profile ones includes Arup’s 2024 attack.

• In this, a member of the engineering company’s financial team was added to a video call with whom he believed was the CFO and other staff.
• Since the accountant recognized some of these authority figures, he agreed to send over 200 million HKD – or $25.6 million USD – to the specified bank accounts.

All members of the call, other than the finance team member, were deepfake re-creations.

Best Practices to Prevent an AI Cyber Attack

Preventing AI cyber attacks demands that a roster of cybersecurity best practices be upheld.

While many of these will generally reduce the threat profile from any cyber attack, the overwhelming focus is on cutting the security team’s Mean Time to Respond (MTTR), to match the almost-instant pace of AI attacks.

Monitor endpoints and network behavior

Deployed alongside the networks and endpoints of an organization, it’s possible for an AI model to ingest day-to-day user and network behavior. This is then assembled into patterns of normal endpoint behavior (like user activity, process execution, or file access) and network traffic (such as connection patterns and data flows).

Establishing this baseline of those behaviors allows those same ML algorithms to identify outliers:

• Unusual login times
• Data transfers
• Access requests

That could indicate malware, insider threats, or command-and-control activity in real time.

Provide Vulnerable Employees with In-Depth Phishing Training

Organizations should prioritize in-depth phishing training for employees who are most susceptible to social engineering tactics, particularly as AI-powered attacks grow in sophistication. Integrating a dedicated module into existing security awareness programs can address the nature of phishing threats.

These modules should emphasize how generative AI is used to craft:

• Highly convincing emails, messages
• Real-time chat interactions that mimic legitimate communication.

By understanding how attackers use language models to personalize attacks, employees can better identify red flags and reduce the risk of compromise.

Also, as deepfake technology becomes more accessible, training should include examples of these tactics and provide guidance on verifying identities and spotting anomalies in AI-generated media. Empowering employees with this builds a critical layer of human defense.

Implement Automated Response Capabilities

Once a threat is detected, an appropriately-provisioned AI security tool can automatically trigger predefined security protocols. This can include a wide variety of different actions, and is limited essentially by the other security tools it can integrate with, for instance:

• It can block suspicious IP addresses via the organization’s firewall
• Reset compromised user credentials via the Identity and Access Management system

This automation reduces the time between detection and response and minimizes any potential damage from cyberattacks.

Consider a Regulatory Framework

Maintaining an overview of how well secured your organization is can be difficult.

Rather than figuring it out in isolation, security leaders should look to a corresponding regulatory framework. These frameworks can precisely identify which best practices should be prioritized.

Frameworks can be particularly vital for larger organizations that need to establish and enforce a global standard: they ensure consistent implementation even across multiple countries and prevent gaps from sprouting between different offices.

From an operational perspective, it also helps align stakeholders that aren’t on the front lines of cybersecurity, ensuring AI safety measures are adopted broadly