Security for AI: Governing Modern AI Systems

Agenda for the Masterclass

Why AI Security Matters
- AI adoption in modern organizations
- AI as a business, security, privacy, and compliance risk
- Difference between Security for AI and AI for Security
- Role of GRC in responsible AI adoption
Security for AI
- What needs to be secured in AI systems
- AI applications
- Prompts and inputs
- AI models
- Training and reference data
- APIs and integrations
- AI agents and automation
- User access and permissions
- Logs and monitoring
Key Risks in AI Systems
- Data leakage
- Prompt injection
- Hallucination
- Bias and unfair outcomes
- Shadow AI
- Third-party AI risk
- Unauthorized access
- Lack of audit trail
- Over-reliance on AI outputs
GRC Controls for Securing AI
- AI usage policy
- Approved AI tools list
- AI system inventory
- AI risk assessment
- Data classification
- Privacy impact assessment
- Vendor risk assessment
- Access control
- Human review
- Logging and monitoring
- Incident response process
- Evidence retention
AI Governance Model
- AI governance committee
- Business owner accountability
- Security team involvement
- Privacy and legal review
- Compliance and audit oversight
- Risk classification of AI use cases
- Approval process for high-risk AI
- Periodic review and reassessment
AI for Security
- AI in cybersecurity operations
- AI in GRC operations
- AI as a decision-support tool
- Human-in-the-loop approach
- Using AI to improve speed, visibility, consistency, and reporting
AI Use Cases in Security and GRC
- Threat detection
- Phishing analysis
- Vulnerability prioritization
- Incident response support
- Log and alert summarization
- Security awareness content
- Policy drafting
- Control mapping
- Audit evidence review
- Vendor questionnaire review
- Compliance reporting
Risks of Using AI for Security
- False positives
- False negatives
- Hallucinated recommendations
- Sensitive data exposure
- Poor explainability
- Vendor dependency
- Weak human oversight
- Lack of accountability
- Compliance blind spots
Key Takeaways
- AI must be governed before it is trusted
- Security for AI protects AI systems from misuse and compromise
- AI for Security uses AI to strengthen cybersecurity and GRC
- AI risks must be classified based on business impact
- Human validation is mandatory for important decisions
- AI vendors must be assessed like critical third parties
- AI controls must be monitored and auditable
- AI should support accountability, not replace it

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<ul>
<li>Why AI Security Matters
<ul>
<li>AI adoption in modern organizations</li>
<li>AI as a business, security, privacy, and compliance risk</li>
<li>Difference between Security for AI and AI for Security</li>
<li>Role of GRC in responsible AI adoption</li>
</ul>
</li>
<li>Security for AI
<ul>
<li>What needs to be secured in AI systems</li>
<li>AI applications</li>
<li>Prompts and inputs</li>
<li>AI models</li>
<li>Training and reference data</li>
<li>APIs and integrations</li>
<li>AI agents and automation</li>
<li>User access and permissions</li>
<li>Logs and monitoring</li>
</ul>
</li>
<li>Key Risks in AI Systems
<ul>
<li>Data leakage</li>
<li>Prompt injection</li>
<li>Hallucination</li>
<li>Bias and unfair outcomes</li>
<li>Shadow AI</li>
<li>Third-party AI risk</li>
<li>Unauthorized access</li>
<li>Lack of audit trail</li>
<li>Over-reliance on AI outputs</li>
</ul>
</li>
<li>GRC Controls for Securing AI
<ul>
<li>AI usage policy</li>
<li>Approved AI tools list</li>
<li>AI system inventory</li>
<li>AI risk assessment</li>
<li>Data classification</li>
<li>Privacy impact assessment</li>
<li>Vendor risk assessment</li>
<li>Access control</li>
<li>Human review</li>
<li>Logging and monitoring</li>
<li>Incident response process</li>
<li>Evidence retention</li>
</ul>
</li>
<li>AI Governance Model
<ul>
<li>AI governance committee</li>
<li>Business owner accountability</li>
<li>Security team involvement</li>
<li>Privacy and legal review</li>
<li>Compliance and audit oversight</li>
<li>Risk classification of AI use cases</li>
<li>Approval process for high-risk AI</li>
<li>Periodic review and reassessment</li>
</ul>
</li>
<li>AI for Security
<ul>
<li>AI in cybersecurity operations</li>
<li>AI in GRC operations</li>
<li>AI as a decision-support tool</li>
<li>Human-in-the-loop approach</li>
<li>Using AI to improve speed, visibility, consistency, and reporting</li>
</ul>
</li>
<li>AI Use Cases in Security and GRC
<ul>
<li>Threat detection</li>
<li>Phishing analysis</li>
<li>Vulnerability prioritization</li>
<li>Incident response support</li>
<li>Log and alert summarization</li>
<li>Security awareness content</li>
<li>Policy drafting</li>
<li>Control mapping</li>
<li>Audit evidence review</li>
<li>Vendor questionnaire review</li>
<li>Compliance reporting</li>
</ul>
</li>
<li>Risks of Using AI for Security
<ul>
<li>False positives</li>
<li>False negatives</li>
<li>Hallucinated recommendations</li>
<li>Sensitive data exposure</li>
<li>Poor explainability</li>
<li>Vendor dependency</li>
<li>Weak human oversight</li>
<li>Lack of accountability</li>
<li>Compliance blind spots</li>
</ul>
</li>
<li>Key Takeaways
<ul>
<li>AI must be governed before it is trusted</li>
<li>Security for AI protects AI systems from misuse and compromise</li>
<li>AI for Security uses AI to strengthen cybersecurity and GRC</li>
<li>AI risks must be classified based on business impact</li>
<li>Human validation is mandatory for important decisions</li>
<li>AI vendors must be assessed like critical third parties</li>
<li>AI controls must be monitored and auditable</li>
<li>AI should support accountability, not replace it</li>
</ul>
</li>
</ul>

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203 People have registered