AI Risk & Governance Awareness Bootcamp

The AI Risk & Governance Awareness Training is an intensive, 1-hour program designed for all employees and managers who interact with, or plan to use, artificial intelligence tools in their daily operations. Moving AI governance from an isolated IT issue to a shared organizational responsibility, this course empowers participants to safely navigate the modern AI landscape. Attendees will discover the Four Pillars of AI Governance, analyze high-profile real-world data breaches, study global compliance frameworks (including GDPR, India’s DPDPA, and ISO/IEC 42001), and master actionable defense strategies against advanced threats like Shadow AI, prompt injections, and deepfake fraud.

• Welcome & Framework: Understanding AI governance as the policies, controls, and habits ensuring ethical and legal AI use.
• The Four Pillars: Breaking down Safety (preventing harm), Accountability (human ownership), Transparency (disclosing AI use), and Data Protection (controlling access).
• The Urgency Drivers: Evaluating the regulatory, business, and reputational pressures making governance mandatory today.

• Case Study Analysis: Reviewing historical incidents like the Samsung ChatGPT leak, the Arup $25.6M deepfake CFO scam, and the DeepSeek exposed database.
• Human-in-the-Loop Failures: Investigating automated resume-screening bias mimicking Amazon’s real-world hiring tool failures.
• Key Takeaway Lessons: Establishing the golden rules of verification and secure data entry.

• International Standards: Overview of ISO/IEC 42001 (AI lifecycle management) and ISO/IEC 27001 (information security foundation).
• Risk Frameworks: Implementing the US NIST AI Risk Management Framework via the Govern, Map, Measure, and Manage loop.
• Data Privacy Laws: Complying with extraterritorial GDPR regulations and India's DPDPA 2023 data fiduciary obligations.
• Vendor Vetting: Requiring SOC 2 service organization control independent audits before third-party tool adoption.

• PII Classification: Separating standard Personally Identifiable Information from High-Risk Sensitive PII (biometrics, financial records, government IDs).
• Lifecycle Exploitation: Tracking where data vulnerabilities enter during the input, processing, training, and downstream use stages.
• Corporate AI Pitfalls:
  • Hallucinations: Fact-checking confident but completely fabricated AI citations or metrics.
  • Over-reliance: Ensuring managers avoid rubber-stamping unverified AI outcomes.
  • Bias & Unfairness: Preventing the replication of historical dataset discrimination.

• Shadow AI: Identifying hidden vulnerabilities created by unapproved browser extensions, plugins, and personal accounts used for work tasks.
• Prompt Injection: Safeguarding AI assistants from hidden, invisible instructions embedded within contaminated incoming documents or emails.
• AI-Powered Social Engineering: Spotting the flawless grammar, hyper-personalized messaging, and contextual intelligence of modern phishing scams.
• Voice Cloning & Media Deepfakes: Recognizing emergency red flags in audio or video requests demanding secret or hurried financial transactions.

• Everyday Account Discipline: Deploying unique, long passphrases and enforcing multi-factor authentication (MFA) on all AI endpoints.
• The Corporate Six-Pillar Framework: Reviewing internal policies on acceptable use, data classification workflows, and monitoring audits.
• Roles & Responsibilities: Differentiating individual employee duties from manager escalation requirements.
• Emergency Incident Reporting: Following the 4-step rapid response loop (Stop the action, Don't delete evidence, Report to IT security immediately, Follow up).