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Alex Delaney

Alex Delaney

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A slide titled 'Risks, Security, and Governance' lists AI risks like 'Hallucination' and 'Prompt Injection' on the left and their corresponding mitigations like 'Verification & citations' and 'Sandboxed tools' on the right, connected by animated lines.
A slide titled 'Risks, Security, and Governance' lists AI risks like 'Hallucination' and 'Prompt Injection' on the left and their corresponding mitigations like 'Verification & citations' and 'Sandboxed tools' on the right, connected by animated lines. Fragment #1A slide titled 'Risks, Security, and Governance' lists AI risks like 'Hallucination' and 'Prompt Injection' on the left and their corresponding mitigations like 'Verification & citations' and 'Sandboxed tools' on the right, connected by animated lines. Fragment #2A slide titled 'Risks, Security, and Governance' lists AI risks like 'Hallucination' and 'Prompt Injection' on the left and their corresponding mitigations like 'Verification & citations' and 'Sandboxed tools' on the right, connected by animated lines. Fragment #3
This slide was generated for the topic:

Mapping AI System Risks to Concrete Mitigation Strategies for Enterprise Governance and Security

Description provided by the user:

The user requested a slide for a business presentation aimed at stakeholders concerned with AI adoption. The goal was to demonstrate a robust and structured approach to managing the inherent risks of implementing large language models (LLMs) and other AI systems. The slide needed to be clear, professional, and reassuring. It should map specific, well-known AI risks (like hallucination and data leakage) to tangible technical solutions and controls, while also linking these efforts to established compliance frameworks like SOC 2 and GDPR to build trust with a corporate audience.

Categories

Business & StrategyTechnology & SoftwarePresentations & StorytellingData & Visualization

Generated Notes

First, set the frame: we’re mapping concrete AI risks to concrete mitigations so governance is actionable, not abstract. On the first reveal, call out the left column. Each risk appears in muted red to signal exposure: hallucination, prompt injection, data leakage, copyright, privacy, and model collapse. Next, bring in the right column. As mitigations slide in, note how the risks settle to neutral — the goal is to move from alarm to assurance. Walk each pair briefly: Hallucination maps to verification and citations. Prompt injection is contained by sandboxed tools and allowlists. Data leakage is addressed with PII detection and redaction. Copyright concerns use watermarking and provenance. Privacy is enforced through policy filters. Model collapse is resisted with continuous red teaming. Emphasize that the connecting lines represent coverage — every risk has a named control. Close with the compliance posture in the footer: GDPR, SOC 2, ISO 27001 — aligning technical controls to recognized standards.

Behind the Scenes

How AI generated this slide

  1. First, establish the slide's narrative structure: problem (risk) followed by solution (mitigation). This is achieved using a two-column layout and a multi-step animation sequence.
  2. Define the core data as an array of risk-mitigation pairs, making the content easy to manage and render dynamically. This list includes key AI safety topics like Hallucination, Prompt Injection, and Model Collapse.
  3. Implement a two-stage reveal using Framer Motion and a state variable `isMitigationVisible`. The first animation fragment reveals the title and the list of risks in a cautionary red color.
  4. The second fragment triggers a state change, which animates the appearance of the mitigations, draws a connecting line, and visually transitions the risk text from red to a neutral color, symbolizing the neutralization of the threat.
  5. Add visual reinforcement with custom SVG icons (a shield for risks, a lock for mitigations) and a footer containing compliance badges (GDPR, SOC 2) to ground the technical controls in recognized business standards.

Why this slide works

This slide is highly effective because it translates abstract AI governance concepts into a clear, visually compelling diagram. The animated reveal tells a powerful story, moving the audience from identifying a problem (the list of risks) to seeing a solution (the mitigations appearing). This problem-solution framing is persuasive and builds confidence. Using staggered animations (`delay`) for each row makes the information easier to digest. The color shift from a cautionary rose to a neutral slate visually reinforces the message that risks are being addressed. The inclusion of enterprise compliance standards like SOC 2 and ISO 27001 in the footer directly addresses the concerns of a B2B audience, making the presentation more credible and commercially relevant.

Frequently Asked Questions

What is the purpose of the two-step animation on this slide?

The two-step animation serves a critical storytelling purpose. First, it introduces the risks alone, allowing the audience to focus on the challenges and potential problems. This sets the stage. Then, in the second step, it reveals the mitigations. This 'problem-then-solution' sequence is a classic persuasive technique that transforms a slide from a simple list into a narrative of control and competence, moving the audience from a state of concern to one of reassurance.

How does this slide connect technical AI controls to business compliance?

The slide masterfully bridges the gap between technical AI safety measures and business-level compliance requirements. While the main body details specific technical controls like 'PII detection & redaction' and 'Sandboxed tools', the footer explicitly lists recognized compliance standards such as 'GDPR', 'SOC 2', and 'ISO 27001'. This visually and conceptually links the technical implementations to the formal governance frameworks that business leaders, legal teams, and enterprise customers care about, demonstrating a mature and comprehensive approach to risk management.

What is 'Model Collapse' and how is it mitigated?

'Model Collapse' is a potential long-term risk where AI models trained on synthetic, AI-generated data begin to lose quality and diversity, eventually producing degraded or nonsensical outputs. The slide suggests 'Continuous red teaming' as a mitigation. This is a proactive security practice where a dedicated team acts as an adversary, constantly testing and trying to break the model to identify its weaknesses, biases, and potential for degradation before they become major issues in a live environment.

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