Analysing the risks and rewards of leveraging AI in cyber threat triage and response

Slide 1: Title

• Title: "Risks of Using AI in Cyber Threat Triage and Response"
• Subtitle: "Exploring the Pros and Cons"

Slide 2: Introduction

• Brief introduction to the increasing reliance on AI in cybersecurity.
• Importance of AI in threat triage and response.

Slide 3: Pros of Using AI

• Efficiency: Faster threat detection and response.
• Automation: Reduces manual workload, allowing analysts to focus on more complex tasks.
• Pattern Recognition: AI excels in identifying patterns and anomalies in large datasets.
• 24/7 Monitoring: AI systems can provide continuous monitoring without human fatigue.

Slide 4: Cons of Using AI

• False Positives: AI may generate false alarms, leading to wasted resources.
• Adversarial Attacks: AI systems can be manipulated by attackers to evade detection.
• Bias: Pre-existing biases in AI algorithms can result in discriminatory outcomes.
• Complexity: Managing and understanding complex AI systems can be challenging.

Slide 5: Security Risks

• Vulnerabilities: AI systems themselves can be vulnerable to cyberattacks.
• Data Privacy: Handling sensitive data in AI models may pose privacy concerns.
• Over-reliance: Blind trust in AI may lead to neglect of human intuition and oversight.
• Slide 6: Ethical Considerations
• Transparency: Lack of transparency in AI decision-making processes.
• Accountability: Determining responsibility in the event of AI failure.
• Job Displacement: Automation of tasks may lead to job loss for cybersecurity professionals.

Slide 7: Regulatory Challenges

• Compliance: Ensuring AI systems comply with relevant regulations.
• Legal Implications: Addressing legal challenges associated with AI-driven decisions.
• International Standards: Lack of standardised regulations for AI in cybersecurity.

Slide 8: Mitigation Strategies

• Human Oversight: Incorporate human judgment in decision-making.
• Regular Audits: Periodic evaluations of AI systems for vulnerabilities and biases.
• Training Data Quality: Ensure diverse and unbiased training datasets.
• Regulatory Compliance: Adhere to existing and emerging regulations.

Slide 9: Conclusion

• Summarise key points
• Emphasis on the need for a balanced approach in leveraging AI in cybersecurity.

Slide 10: Q&A