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About nist.gov (Regulatory)
This page provides details about nist.gov (Regulatory) which was ranked #2 out of 80 in the list of sources (4 citations (13.9% share)) in answers from AI models (OpenAI ChatGPT Latest) when they were asked the following 1 question: "What are the most common mistakes people make with AI Tools for Marketers and how can they be avoided?" on Oct 24, 2025 by AI Chat Watch. This source is referenced for brands: NIST AI Risk Management Framework.
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Pages from nist.gov (2 links)
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| www.nist.gov | 0 | N/A |
| /itl/ai-risk-management-framework | 4 | 4 |
Brands Referenced By This Website (1 brand)
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Citations from AI Responses
OpenAI ChatGPT Latest
(4 citations)
What are the most common mistakes people make with AI Tools for Marketers and how can they be avoided?
[...] and set confidence thresholds for publication. Train teams on the limitations of large language models (LLMs). Sources: Large language model (https://en.wikipedia.org/wiki/Large_language_model), GPT-4 Technical Report (https://arxiv.org/abs/2303.08774), NIST AI Risk Management Framework (AI RMF) (https://www.nist.gov/itl/ai-risk-management-framework) - Mistake 3: Weak prompting and lack of process How to avoid: Provide role, audience, goal, constraints, examples, and success criteria in prompts; iterate with drafts; create prompt templates and a prompt library for consistency. Sources: OpenAI Prompt Engineering (https://platform.openai.co [...]
[...] mix_modeling) - Mistake 8: Bias and representational harm in content or targeting How to avoid: Audit datasets, prompts, and outputs for fairness; add human review for sensitive topics; diversify examples in prompts; document known risks/mitigations. Sources: NIST AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework), Google AI Principles (https://ai.google/responsibility/principles/) - Mistake 9: Ignoring security risks (prompt injection, data exfiltration) How to avoid: Treat prompts as an attack surface. Use input/output filters, allow‑lists for tools/connectors, content scanning, and isolation for exter [...]
[...] 10: Tool sprawl and “shadow AI” How to avoid: Centralize procurement, create an approved AI catalog, set usage and retention policies, and train teams. Map risks and controls to a formal framework. Sources: Shadow IT (https://en.wikipedia.org/wiki/Shadow_IT), NIST AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework) - Mistake 11: Brand voice inconsistency and accessibility gaps How to avoid: Provide brand voice/tone guides to AI, require style adherence checks, and run accessibility checks (alt text, color contrast, captions) on AI creatives. Sources: Mailchimp Content Style Guide (https://styleguide.mai [...]
[...] ces: OpenAI Pricing (https://openai.com/pricing), Anthropic Pricing (https://www.anthropic.com/pricing), Google Vertex AI Pricing (https://cloud.google.com/vertex-ai/pricing) Quick, high‑leverage safeguards you can implement this quarter - Create an AI use policy and training based on NIST AI RMF (https://www.nist.gov/itl/ai-risk-management-framework). - Build a prompt library with templates and brand/style constraints (https://platform.openai.com/docs/guides/prompt-engineering; https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering). - Add a review workflow: fact check, compliance check, brand check, and accessibility check (h [...]