AI in cybersecurity challenges has introduced new, complex threats that target both systems and individuals. Cybercriminals now use AI to create highly convincing attacks that often evade traditional defences. Understanding these emerging risks is essential for building strong security practices.
Recognising social engineering risks with AI in cybersecurity
Imagine receiving an urgent email from your supervisor abroad, mentioning their location and time zone. Moments later, you receive a call, and a familiar voice confirms the email’s details, urging you to act quickly. This scenario could be an AI-driven social engineering attack, where cybercriminals impersonate trusted individuals. Such impersonation requests often involve sensitive actions, like fund transfers or data access.
Since 2019, cases of this nature have surged, with voice replication technology improving dramatically. AI enables attackers to use realistic voice synthesis, making impersonation difficult to detect and escalating the risk of deception.
Types of AI-powered cyber attacks
AI-powered attacks vary in form and present new challenges for cybersecurity teams. Here’s an overview of some key threats.
Deepfake attacks
Cybercriminals use deepfake technology to create fake audio and video that convincingly impersonate individuals. This manipulation can deceive victims into revealing sensitive information or performing harmful actions.
AI phishing attacks
AI technology now powers more sophisticated phishing attacks. Attackers use it to generate emails or websites that look authentic, tricking users into believing they are accessing trusted resources. With AI, attackers can clone websites within seconds.
Denial of service attacks
AI has made distributed denial of service (DDoS) attacks more powerful. These attacks flood targets with traffic, disrupting services and causing downtime. AI adds precision to DDoS attacks, amplifying their effectiveness.
AI-powered ransomware
Hackers personalise ransomware attacks by identifying specific targets, including entire organisations. AI aids in email tracking and personalisation, making phishing emails harder to detect and bypassing defences.
Advanced persistent threats (APTs)
AI enables long-term, advanced persistent threats, where attackers infiltrate networks to steal information over time. This persistence challenges traditional monitoring and response methods.
Analysing data for targeted attacks
Cybercriminals use machine learning algorithms to analyse data, identifying patterns to enhance targeting accuracy. This data-driven approach allows them to exploit vulnerabilities more effectively.
Strengthening defences with the SOUP-D framework
To counter AI-driven threats, organisations can adopt the SOUP-D framework:
- Safeguard: Back up critical data regularly for easy recovery after an attack.
- Origin: Verify the source of all contacts, especially online. Confirm legitimacy through alternative methods.
- Update: Keep devices, software, and antivirus programs up to date, minimising vulnerabilities.
- Password: Use strong, unique passwords and enable multi-factor authentication for enhanced security.
- Do not trust: Remain cautious of unsolicited requests involving sensitive data. Confirm requests through independent, reliable sources.
