Introduction

Over the past three months, a few friends and I have immersed ourselves in understanding ChatGPT and exploring how to effectively utilize AI in our lives. Along the way, we've uncovered an uncomfortable truth: AI is not just another tool. It's a multiplier. A mirror. A risk multiplier that most of us aren't ready to handle.

Below is a consolidated list of the AI risk vectors we’ve uncovered. Some are cultural. And some are legal and existential.

My friends and I are conducting in-depth analyses of these risks and will post them when they are ready. Please share any risks you have uncovered.

Prompt Injection & Embedded Hijacks

Attackers manipulate prompts within documents, code, or webpages to redirect AI behavior in unintended ways.

Synthetic Output Feedback Loops

AI-generated content re-enters official systems (e.g., CRMs, policy documents), distorting the truth over time like a game of telephone.

Hallucination in High-Stakes Contexts

LLMs confidently fabricate plausible but false facts, legal justifications, and audit reports.

Unsecured Copilot & Plugin Integration

Developer copilots and workplace assistants bypass review processes, introducing risks into code, cloud configurations, and operations scripts.

Confidential Data Leakage via External Tools

Sensitive enterprise data copied into unsecured AI chat tools risks contamination from external training or data breaches.

Adversarial Data Poisoning

Attackers inject misleading or biased data into AI training sets through open forms, reviews, and community contributions.

Echo Chamber Amplification

AI systems trained exclusively on internal material risk perpetuate legacy assumptions, biases, and obsolete practices.

Regulatory Ambiguity Around Authorship

AI-generated policies, reports, and decisions blur the lines of attribution, raising crucial questions about accountability and auditability.

Emotional Framing and the Uncanny Voice

The use of emotional language (”happily," "I believe") by AI creates a false sense of intimacy and potential misinterpretation in corporate settings.

Insider Threat via Personalized AI Agents

Malicious insiders leverage AI personalization to learn internal processes or create more convincing phishing targets rapidly.

Over-Reliance on AI in Risk and Compliance

Excessive trust in AI for detecting anomalies or drafting controls can leave organizations blind to subtle or undocumented risks.

AI-Powered Social Engineering

Attackers harness generative AI to create sophisticated, multilingual phishing attempts and impersonations, including voice.

Loss of Institutional Memory

As workflows shift to prompt-based tools, permanent knowledge gives way to temporary query-response patterns, raising questions about the long-term retention of knowledge.

Model Drift and Unmonitored Tuning

Uncontrolled fine-tuning of local models risks reintroducing outdated logic, biased outputs, or unauthorized behavior.

Human-AI Misuse in Sensitive Contexts

Employees seeking AI guidance for legal, medical, or emotional matters create ethical and liability risks for organizations.

Recommendations (Initial Steps)

• Create an internal AI Risk Register
• Implement prompt injection red-teaming
• Require human-in-the-loop review for critical AI-generated content
• Audit third-party AI tool usage across the enterprise

🧭 Closing Note: This Isn’t Fear. It’s Foresight.

These scenarios aren't hypothetical—they're already happening. We don't need panic; we need pattern recognition and preparation. Organizations that understand and act on this shift won't merely avoid risk. They'll lead the next phase of responsible intelligence.

This article is part of the “Field Notes from the Interface” series—exploring how humans and AI are learning to work, think, and build together.