Artificial intelligence is now central to enterprise innovation, risk reduction, and profitability—making legal, regulatory, and risk preparedness a top priority. This presentation explores the AI lifecycle from inception to deployment, highlighting how implementations can be compromised through inadvertence, internal misuse, or external threats. We’ll examine systemic risks across the AI ecosystem and outline practical mitigation strategies. The session concludes with an overview of AI forensics—what to collect, how to do so defensibly, and its role in investigations, litigation, and audits.

Description

Artificial intelligence has become the new norm for enterprise competitive advantage, decreased risk and improved profit. Accordingly, we must be prepared for regulatory, legal and risk as a top priority.

In this presentation, Paul will cover the AI ecosystem, from inception, to development and then to deployment.

From this, we will examine ways in which artificial intelligence implementations can be compromised either through inadvertence or malfeasance. Artificial intelligence risks span the entirety of an ecosystem involving an interdisciplinary synergy that must be examined holistically.

This approach involves first understanding the ways in which AI implementations can be compromised by inadvertence, internal attacks, external threats. We will examine known risks as well as mitigation strategies to reduce risk across the AI-technology spectrum.

We will then review AI forensics which touches on what information should be gathered and how to do so in a forensically sound and defensible manner. This is most relevant as factual support for investigations, discovery in litigation and in audits.

Key Takeaways for Risk Professionals

AI Is a Risk Vector: AI systems introduce unique risks—legal, operational, ethical—that must be integrated into enterprise risk frameworks.

End-to-End Exposure: Risks can arise at any stage—design, development, or deployment—and require continuous, interdisciplinary oversight.

Compromise Is Multidimensional: AI can be undermined through inadvertent design flaws, insider misuse, or external attacks; vigilance must extend beyond traditional cyber controls.

Holistic Risk Mitigation: Effective controls include technical safeguards, governance policies, cross-functional coordination, and continuous monitoring.

AI Forensics Matters: In the event of an incident, knowing what data to preserve and how to collect it forensically is crucial for audits, investigations, and litigation.

Prepare for Regulatory Scrutiny: Emerging global regulations demand documentation, explainability, and defensible processes—risk teams must lead in ensuring compliance.