AI Advisory: Good vs Bad Harness Engineering Highlights Risks of Over‑Prescriptive Prompts
What Happened — Daniel Miessler published a thought‑leadership piece outlining “Good” and “Bad” harness engineering for generative AI. He argues that overly prescriptive prompts (bad harness) hinder AI performance, while context‑rich, outcome‑focused prompts (good harness) enable safer, more reliable outputs.
Why It Matters for TPRM —
- Over‑prescriptive AI instructions can embed hidden biases and create compliance blind spots.
- Context‑rich harnesses improve auditability of AI decisions, supporting governance and risk controls.
- Mis‑engineered prompts may lead to data leakage or unintended actions, affecting third‑party risk exposure.
Who Is Affected — Technology SaaS providers, AI platform vendors, enterprises integrating generative AI, and any MSPs delivering AI‑enabled services.
Recommended Actions —
- Review AI prompt libraries for prescriptive patterns and replace with context‑rich harnesses.
- Update AI governance policies to require “Bitter Lesson” alignment (focus on what vs how).
- Conduct a risk assessment of AI‑driven workflows for potential compliance or data‑exfiltration impacts.
Technical Notes — The article references Richard Sutton’s “Bitter Lesson” (2019) as the theoretical basis. No CVEs, exploits, or technical vulnerabilities are disclosed; the risk is procedural and architectural. Source: https://danielmiessler.com/blog/good-and-bad-harness-engineering?utm_source=rss&utm_medium=feed&utm_campaign=website