Cyber Attacks

Cyber attacks are malicious attempts to access, damage, or disrupt systems, networks, or data.

1. Phishing Attacks
   • What it is: Attackers use deceptive emails to steal credentials or deploy malware.
   • Why it matters: AI-powered automation enhances phishing sophistication.
   • How to prevent it: AI-driven email security, user training, and multi-factor authentication (MFA).

2. Ransomware
   • What it is: Malware encrypts AI models or training data, demanding payment.
   • Why it matters: AI-dependent businesses risk model corruption and financial loss.
   • How to prevent it: Secure offline backups, endpoint protection, and zero-trust access.

3. Denial-of-Service (DoS) Attacks
   • What it is: Overloading AI APIs or inference systems to cause failures.
   • Why it matters: Real-time AI services (finance, healthcare) must remain online.
   • How to prevent it: Rate limiting, cloud-based DoS protection, AI-driven anomaly detection.

4. Man-in-the-Middle (MitM) Attacks
   • What it is: Intercepting AI model inputs or outputs to alter decisions.
   • Why it matters: AI-driven automation in finance, healthcare, and security can be compromised.
   • How to prevent it: End-to-end encryption, TLS 1.3, AI model watermarking.

5. SQL Injection
   • What it is: Attackers manipulate AI databases to alter training data.
   • Why it matters: Corrupt training data skews AI decision-making.
   • How to prevent it: Parameterized queries, strict database access controls.

6. Cross-Site Scripting (XSS)
   • What it is: Injecting malicious scripts into AI-powered interfaces.
   • Why it matters: AI chatbots and LLM-driven apps can be hijacked.
   • How to prevent it: Input sanitization, Content Security Policy (CSP), AI-based anomaly detection.

7. Zero-Day Exploits
   • What it is: Exploiting unknown AI system vulnerabilities.
   • Why it matters: Zero-day attacks on AI can lead to data breaches, fraud, or misinformation.
   • How to prevent it: Threat intelligence tools, security patches, AI-driven attack simulations.

8. DNS Spoofing
   • What it is: Manipulating DNS records to reroute users to fake AI platforms.
   • Why it matters: Attackers can steal credentials or inject adversarial inputs into AI models.
   • How to prevent it: DNSSEC, AI-driven DNS monitoring, endpoint verification.