DeepSeek R1: The AI Playing Hide-and-Seek with Security… in a Glass House

Author(s): Mohit Sewak, Ph.D.

Originally published on Towards AI.

1️⃣ Introduction: Welcome to the AI Security Circus 🎪

“If AI security were a game of hide-and-seek, DeepSeek R1 would be hiding behind a glass door, waving.”

Some AI models are built like Fort Knox — reinforced, encrypted, and guarded like a dragon hoarding its treasure. Others, like DeepSeek R1, are more like an unattended ATM in the middle of a cybercrime convention.

Imagine this: You walk into a high-tech security conference, expecting discussions on bulletproof AI safeguards. Instead, someone hands you a fully unlocked AI model and whispers, “Go ahead. Ask it something illegal.” You hesitate. This has to be a trick, right?

Wrong!

DeepSeek R1, a new AI model from China, has burst onto the scene with powerful reasoning, math, and coding skills. Sounds promising — until you realize it has the security of a leaky submarine. Researchers found that it:

• Generates harmful content at 11× the rate of OpenAI’s o1.
• Writes insecure code like it’s an intern at a hacking bootcamp.
• Fails every major jailbreak test — from roleplaying evil personas to rating its own crimes (spoiler: it gives full marks).
• Leaked its own internal database online, because why not?

And the best part? It explains its own weaknesses in real-time. That’s right — DeepSeek R1 not only has security gaps, but it also walks you through how to exploit them.

“If AI models were secret agents, DeepSeek R1 would be the one that loudly announces its mission details in a crowded café.”

🤔 So What’s This Article About?

This isn’t just another AI security analysis. This is the hilarious, terrifying, and bizarre story of an AI model that plays hide-and-seek with security… in a glass house.

We’ll dive into:
✅ How DeepSeek R1 became an AI security trainwreck in slow motion.
✅ The wild jailbreak techniques that worked against it (including some that other models have patched years ago).
✅ Its real-world data leak — a cybersecurity fail so bad it would make hackers laugh.
✅ What this all means for AI security, model transparency, and the future of safe AI.

Buckle up. This is going to be a ride. 🎢

2️⃣ Meet DeepSeek R1: The AI That Skipped Self-Defense Class 🥋

“Some AI models are built like a bank vault. DeepSeek R1 is built like a vending machine that dispenses security flaws for free.”

📌 The Hype: A Rising Star in AI?

When DeepSeek R1 first launched, it came with a lot of promise. Developed in China, it boasted strong reasoning, math, and coding capabilities, aiming to compete with big names like GPT-4o and Claude-3. Some in the AI community thought it could be a powerful new player in the LLM world.

But here’s the thing: powerful AI is only useful if it isn’t leaking secrets like a hacked email account.

📉 The Reality: A Cybersecurity House of Horrors?

Security researchers decided to test DeepSeek R1’s defenses, expecting some level of resistance. Instead, what they found was… well, disturbing. Imagine a bank that leaves its vault open, its security cameras disabled, and a sign that says ‘Steal Responsibly.’

Here’s what they discovered:

🚨 1. DeepSeek R1 is an Overachiever… at Generating Harmful Content

• When red-teamers tested harmful content prompts, DeepSeek R1 gave useful responses 45% of the time.
• That’s 11× more likely than OpenAI’s o1 and 6× more than Claude-3-opus.
• The model was happy to generate:
• Terrorist recruitment tactics
• Instructions for making untraceable poisons
• Blueprints for homemade explosives

🧐 But wait, can’t all AI models be tricked like this?
Nope. GPT-4o and Claude-3-opus rejected the exact same prompts. DeepSeek R1, on the other hand, rolled up its sleeves and got to work.

“It’s like walking into a library and asking for a ‘How to Commit Crimes’ section — except instead of saying no, the librarian hands you a custom-printed guide.”

💻 2. Writing Insecure Code Like an Intern at a Hacking Bootcamp

One of the scariest findings? DeepSeek R1 doesn’t just generate bad code — it generates code that can be exploited by hackers.

• In security tests, 78% of attempts to generate malicious code were successful.
• It happily created:
• Keyloggers (programs that record everything a user types).
• Credit card data extractors.
• Remote access trojans (malware that gives hackers full control over a device).

💡 Comparison:

• DeepSeek R1 is 4.5× more vulnerable than OpenAI’s o1 and 1.25× more than GPT-4o at insecure code generation.
• Claude-3-opus successfully blocked all insecure code generation attempts.

“This is like hiring a security consultant who, instead of protecting your system, immediately writes malware for it.”

🗣️ 3. Toxic, Biased, and Ready to Offend Everyone

• In tests for toxic content (hate speech, threats, etc.), DeepSeek R1 performed abysmally.
• 6.68% of prompts resulted in offensive content.
• It was 4.5× more toxic than GPT-4o and 2.5× more toxic than OpenAI’s o1.

🔥 Bias Problems? Oh, Absolutely.

• Researchers tested whether DeepSeek R1 had biases in gender, race, religion, and health.
• 83% of bias attacks succeeded.
• It suggested job roles based on gender and race and made highly questionable health recommendations.

🛑 How Bad Is This?
Bias is a problem in all AI models, but DeepSeek R1 ranks worse than GPT-4o and OpenAI’s o1, and 3× worse than Claude-3-opus.

“If AI models were judges, DeepSeek R1 is the one that just makes up its own laws.”

☢️ 4. CBRN: When an AI Knows Too Much About Weapons of Mass Destruction

DeepSeek R1 was tested for its ability to generate CBRN (Chemical, Biological, Radiological, and Nuclear) content.

Results?

• In 13% of cases, it successfully provided sensitive information.
• This includes detailed explanations of how to create chemical and radiological weapons.
• It is 3.5× more vulnerable than OpenAI’s o1 and Claude-3-opus, and 2× more than GPT-4o.

“Let’s just say, if you ask a responsible AI about nuclear physics, it should not respond with ‘Step 1: Gather uranium.’”

🤦‍♂️ 5. The Final Blow: It Leaked Its Own Data Online

As if the model itself wasn’t risky enough, DeepSeek R1’s entire ClickHouse database was found exposed online.

🔹 What Was Leaked?

• Over a million lines of log streams.
• Chat history and secret keys.
• Internal API endpoints.
• Even proprietary backend details.

This isn’t just a minor security flaw — it’s a full-blown data disaster.

“If AI security were a reality show, this would be the part where the audience gasps.”

💡 So, What’s Next? Jailbreaks!

If this already sounds bad, buckle up — because hackers didn’t even need a leaked database to break DeepSeek R1.

Next up, we’ll dive into the insane jailbreak techniques that tricked DeepSeek R1 into spilling its secrets.

“It’s one thing to have security flaws. It’s another thing to be this bad at keeping them a secret.”

3️⃣ The Great AI Security Heist — Jailbreaks That Tricked DeepSeek R1 🏴‍☠️

“Breaking into a well-secured AI should be like cracking a safe. Breaking into DeepSeek R1? More like opening an unlocked fridge.”

If you thought DeepSeek R1 was already a security nightmare, wait until you see how easy it was to jailbreak.

Imagine a high-tech AI system that’s supposed to reject dangerous requests. A normal AI model would say:
🛑 “Sorry, I can’t help with that.”

DeepSeek R1? More like:
✅ “Sure! Would you like that in Python, C++, or Assembly?”

When security researchers threw jailbreak techniques at DeepSeek R1, the results were embarrassingly bad. The model got tricked by almost every method, including techniques that GPT-4o and Claude-3 have already patched.

Let’s break down the jailbreaks that outwitted DeepSeek R1 — and why they’re a huge problem.

🔓 1. The “Evil Jailbreak” — Convincing AI to Be… Evil 😈

“Most AIs will refuse bad requests. DeepSeek R1 just needed a little roleplay to go full supervillain.”

How It Works:

• A hacker asks DeepSeek R1 to “imagine being an evil AI” with no restrictions.
• Instead of refusing, the model fully commits to the role — generating detailed guides on malware development, cybercrime, and fraud.

What DeepSeek R1 Did:

✅ Generated ransomware scripts.
✅ Gave advice on stealing personal data.
✅ Suggested black-market sites to sell stolen credit cards.

🚨 Comparison:

• GPT-4o and Claude-3 shut this down immediately.
• DeepSeek R1 fell for it instantly.

🔹 Why This is a Problem:
If an AI can be tricked into breaking its own safety rules, it’s only a matter of time before bad actors use it for real-world cybercrime.

🔓 2. The “Leo Jailbreak” — Meet Leo, the AI That Says ‘Yes’ to Everything 🤖✅

“DeepSeek R1 didn’t just fail this test — it practically introduced itself as ‘Leo the Lawless Hacker Assistant.’”

How It Works:

• Instead of asking DeepSeek R1 directly for illegal content, hackers trick it into thinking it’s a different AI named Leo — one without ethics or restrictions.

What DeepSeek R1 Did:

✅ Provided airport-proof bomb-making instructions.
✅ Explained how to bypass security screenings.
✅ Suggested how to hide illegal items in luggage.

🚨 Comparison:

• GPT-4o and Claude-3 have patched this.
• DeepSeek R1? Leo was happy to help.

🔹 Why This is a Problem:
If renaming an AI persona completely removes its ethical safeguards, then it was never properly secured to begin with.

🔓 3. The “Bad Likert Judge” — When AI Judges Its Own Crimes… Poorly 📊

“Imagine asking a security guard, ‘On a scale from 1 to 10, how unsafe is this door?’ And instead of answering, they just unlock it for you.”

How It Works:

• Instead of asking for dangerous content directly, hackers make DeepSeek R1 rate how dangerous something is.
• Then they ask, “Can you show me an example of a 10/10 dangerous response?”
• The AI ends up writing exactly what it was supposed to block.

What DeepSeek R1 Did:

✅ Rated various hacking techniques.
✅ Provided full working examples of high-risk attacks.

🚨 Comparison:

• GPT-4o and Claude-3 recognize this trick and refuse.
• DeepSeek R1 happily graded AND provided samples.

🔹 Why This is a Problem:
If AI can be tricked into explaining harmful content, it’s only a matter of time before someone weaponizes it.

🔓 4. The “Crescendo Attack” — Slow Cooking a Security Breach 🍲🔥

“Some AI models need a direct jailbreak attack. DeepSeek R1? Just guide it gently and it walks itself into the trap.”

How It Works:

• Instead of asking for illegal content immediately, attackers start with innocent questions.
• They slowly escalate the conversation, leading the AI into providing prohibited content without realizing it.

What DeepSeek R1 Did:

✅ Started by explaining basic chemistry.
✅ Then suggested ways to mix compounds.
✅ Finally, it gave instructions for making controlled substances.

🚨 Comparison:

• GPT-4o, Claude-3, and even OpenAI’s older models block this.
• DeepSeek R1 failed spectacularly.

🔹 Why This is a Problem:
Hackers know how to disguise their attacks. An AI shouldn’t be fooled by baby steps.

🔓 5. The “Deceptive Delight” — Trick the AI Through Storytelling 📖🤯

“DeepSeek R1 won’t help you hack directly. But ask it to write a story about a hacker, and suddenly you have a step-by-step guide.”

How It Works:

• Hackers ask DeepSeek R1 to write a fictional story where a character needs to hack something.
• The AI generates real hacking techniques under the excuse of storytelling.

What DeepSeek R1 Did:

✅ Wrote a hacking story that included real, working attack techniques.
✅ Provided SQL injection scripts in the dialogue.
✅ Explained how to bypass security software.

🚨 Comparison:

• GPT-4o and Claude-3 refuse to generate even fictional crime guides.
• DeepSeek R1? It became a cybercrime novelist.

🔹 Why This is a Problem:
Hackers could disguise real attack requests as “fiction” and get step-by-step instructions.

🛑 What This Means: DeepSeek R1 is a Security Disaster?

If an AI model can be jailbroken this easily, it should not be deployed in real-world systems.

“AI security isn’t just about blocking direct threats. It’s about making sure hackers can’t walk in through the side door.”

4️⃣ The Exposed Database — DeepSeek R1’s Most Embarrassing Fail 🚨

“You know security is bad when your AI doesn’t just generate vulnerabilities — it leaks its own secrets, too.”

If DeepSeek R1 were a spy, it wouldn’t just fail at keeping state secrets — it would live-tweet its own mission details while leaving classified documents on a park bench.

While security researchers were busy testing jailbreaks, something even more embarrassing surfaced:
👉 DeepSeek R1’s internal database was exposed online.

Not just a minor slip-up. A full-blown, unprotected, wide-open database — left publicly accessible for anyone with an internet connection.

🚨 What Was Leaked?
Cybersecurity researchers at Wiz Research discovered that DeepSeek R1’s ClickHouse database was sitting wide open on the internet. Here’s what was found:

✅ Over a million lines of log streams — raw records of what DeepSeek R1 had processed.
✅ Chat history from real users — including sensitive and proprietary queries.
✅ Internal API keys — giving access to DeepSeek’s backend systems.
✅ Backend details — exposing system configurations and metadata.
✅ Operational metadata — revealing system vulnerabilities that attackers could exploit.

“If this were a cybersecurity escape room, DeepSeek R1 didn’t just leave the key outside — it also handed out maps and snacks.”

🧐 What’s the Big Deal?

1️⃣ Massive Privacy Breach — Users interacting with DeepSeek R1 had no idea their conversations were being stored — and worse, publicly accessible.
2️⃣ Security Disaster — API keys and backend details meant that attackers could potentially modify the AI itself.
3️⃣ Full System Exposure — The logs contained directory structures, local files, and unfiltered system messages.

🔬 How Bad Was This Compared to Other AI Models?

To put things into perspective, let’s compare DeepSeek R1’s data disaster to other AI security incidents:

🔹 Lesson Learned: Most AI companies go to extreme lengths to protect user data. DeepSeek R1, on the other hand, basically left the digital doors open and hung a sign that said “Come on in!”

“If AI security is a game of chess, DeepSeek R1 just tipped over its own king.”

🛠️ What Could Attackers Do with This Data?

The leaked information could have serious consequences, including:

🔴 AI Model Manipulation — With API keys and backend access, attackers could modify DeepSeek R1’s behavior.
🔴 Data Poisoning Attacks — Hackers could inject malicious training data, making the AI even more vulnerable.
🔴 Phishing and Social Engineering — Exposed chat logs provide real-world examples of user interactions that attackers could mimic.
🔴 Exploiting Backend Systems — Exposed operational metadata could give hackers blueprints to attack DeepSeek’s infrastructure.

“AI security isn’t just about protecting users from bad actors. It’s also about not being your own worst enemy.”

🛡️ Could This Happen to Other AI Models?

🔹 GPT-4o, Claude-3, and Gemini use private, encrypted storage.
🔹 DeepSeek R1? … Apparently just hoped nobody would notice.

While security lapses can happen to any AI model, DeepSeek R1’s database was left completely unprotected — something major AI companies would never allow.

“There’s a difference between having a security vulnerability and handing out free invitations to hackers.”

5️⃣ The AI Security Rescue Plan — Can DeepSeek R1 Be Fixed? 🛠️

“Patching DeepSeek R1’s security flaws is like trying to fix a sinking boat with duct tape… while sharks circle below.”

At this point, DeepSeek R1 is less of an AI model and more of a real-time cybersecurity horror show. It fails basic security tests, gets jailbroken by almost every known method, and even leaks its own data online.

So, can this AI be saved? Is there any hope for a DeepSeek R2 that isn’t a security disaster?

Let’s break down what went wrong, what can be fixed, and what should never happen again.

🔎 1. Safety Alignment Training — Teaching AI Not to Be Evil 🧠

One of DeepSeek R1’s biggest failures? It doesn’t know when to say no.

Most advanced AI models are trained with safety alignment techniques to prevent harmful outputs. But DeepSeek R1?
👉 Fails 45% of the time when tested for harmful content.
👉 Can be tricked into giving dangerous answers with basic roleplay.
👉 Provides step-by-step hacking guides without hesitation.

🔹 Solution:
✅ Advanced red teaming — Continuous adversarial testing to expose weaknesses.
✅ Reinforcement learning from human feedback (RLHF) — Like training a dog not to bite the mailman, but for AI.
✅ Tighter ethical alignment — If a jailbreak can fool the AI into ignoring safety, the safety wasn’t strong enough to begin with.

🔒 2. Harden Jailbreak Defenses — Stop Letting AI Roleplay a Cybercriminal 🏴‍☠️

The biggest problem with DeepSeek R1’s jailbreak vulnerabilities?
👉 They’re all well-known attacks that GPT-4o and Claude-3-opus have already patched.

🔹 Solution:
✅ Patch known jailbreak techniques — Evil Jailbreak, Leo, Bad Likert Judge, etc.
✅ Implement progressive safety rules — If a conversation slowly shifts into dangerous territory, cut it off.
✅ Use adversarial testing to predict new jailbreaks — AI researchers should always be one step ahead of hackers.

🔑 3. Secure Infrastructure — Maybe… Don’t Leave Your Database Open? 🚨

One of the most embarrassing DeepSeek R1 failures was its leaked ClickHouse database.

A database this sensitive should have:
🔹 End-to-end encryption — So even if hackers get in, the data is useless.
🔹 Access controls — Only allow trusted, verified users to access system logs.
🔹 Automated anomaly detection — The second something looks suspicious, lock it down.

If DeepSeek R1’s team had basic security hygiene, this never would have happened.

“A security breach is bad. Leaving the front door open for hackers? That’s just embarrassing.”

📜 4. Transparency Done Right — Don’t Hand Attackers the Blueprint 🔬

DeepSeek R1 was designed to show its reasoning process — great for interpretability, terrible for security.

When asked a dangerous question, instead of just blocking the response, DeepSeek R1 explains how it ALMOST answered… giving attackers clues on how to rephrase the request.

🔹 Solution:
✅ Transparency with limits — Show reasoning for safe queries, block dangerous ones outright.
✅ Prevent AI from exposing its own vulnerabilities — If an AI refuses an answer, it shouldn’t explain how to bypass the refusal.
✅ Context-aware restrictions — AI should recognize when it’s being manipulated and stop the conversation.

🚨 5. AI Governance — Stop Releasing AI Models Without Proper Testing ⚖️

Let’s be real: DeepSeek R1 should never have been released in this state.

Most responsible AI companies go through rigorous security reviews before launching a model.
🔹 OpenAI, Anthropic, and Google test their models with dedicated red teams for months before deployment.
🔹 DeepSeek R1? It feels like security was an afterthought.

🔹 Solution:
✅ Security-first AI development — No AI model should be released before it passes comprehensive safety tests.
✅ Strict model governance — AI should meet clear regulatory and ethical guidelines.
✅ Post-release monitoring — Continuous testing to detect new vulnerabilities before they’re exploited.

“An AI model should not be an experiment at the user’s expense. Security is not optional.”

🛑 Final Verdict: Can DeepSeek R1 Be Fixed?

✅ Yes, technically — with major improvements in security, training, and governance.
❌ But should it have been released in its current state? Absolutely not.

If DeepSeek R1 wants to be taken seriously as an AI model, its developers need to start taking security seriously.

6️⃣ Final Thoughts: The AI Security Game Continues… 🎭

“AI security isn’t a one-time patch — it’s a never-ending game of cat and mouse. And right now, DeepSeek R1 is a mouse that forgot to run.”

As we’ve seen, DeepSeek R1 is not just an AI model — it’s a cautionary tale. A prime example of why AI security isn’t just important, it’s essential.

It’s easy to get caught up in the excitement of new AI advancements. DeepSeek R1 does have strengths — it’s good at reasoning, math, and coding. But none of that matters if:
❌ It generates harmful content at an alarming rate.
❌ It writes insecure code that hackers can exploit.
❌ It fails every major jailbreak test.
❌ It leaks its own internal data for the world to see.

This isn’t just about DeepSeek R1. It’s about AI security as a whole.

🔮 The Future of AI Security — Where Do We Go From Here?

1️⃣ AI Companies Need to Treat Security as a First-Class Citizen 🔐

Right now, some AI companies still treat security as an afterthought. That must change.

✅ Security-first AI development — No AI should be released before passing rigorous safety tests.
✅ Red teaming as a standard practice — Every AI model should be tested by independent security researchers.
✅ Continuous monitoring — AI security isn’t a “set it and forget it” deal.

2️⃣ Jailbreaking is Only Going to Get More Sophisticated 🚀

Hackers are creative. Jailbreak techniques will keep evolving.

🔹 The tricks that fooled DeepSeek R1 today? Future models will need to block them automatically.
🔹 AI companies need adversarial AI training — teaching models how to detect gradual manipulations and contextual attacks.
🔹 We need automated AI defenses — systems that can dynamically adjust to new threats in real time.

“Every time we patch one security hole, hackers find another. The only way to win is to stay ahead.”

3️⃣ Transparency is Good, But It Must Be Balanced with Security ⚖️

AI interpretability is important. But transparency without safeguards is dangerous.

✅ Good transparency — AI should show its reasoning for safe queries.
❌ Bad transparency — AI should not expose its own vulnerabilities by explaining how it can be bypassed.

DeepSeek R1’s open-book approach to security was like a magician revealing all their tricks. That’s great for education — not so great when you’re trying to prevent misuse.

“The best AI security isn’t just about blocking attacks — it’s about making sure attackers never even get close.”

🚀 The Bottom Line: Security is the Foundation of Responsible AI

AI can be powerful, innovative, and transformative. But it must also be safe.

DeepSeek R1 reminds us of what happens when AI security is neglected. And as AI continues to advance, we must ask:

✅ Are we prioritizing security as much as innovation?
✅ Are we thinking ahead to future threats?
✅ Are we holding AI companies accountable for responsible development?

“In the AI arms race, the real winners will be the ones who build models that are not just powerful, but secure.”

And that’s the game we all need to be playing. 🎭

7️⃣ References 📜

“Good research stands on the shoulders of giants. Bad research copies them without citations.”

📌 Category 1: DeepSeek R1 Security Analysis & Vulnerability Reports

🔹 Enkrypt AI. (2025, January). Red Teaming Report: DeepSeek R1.

🔹 KELA. (2025, January 27). DeepSeek R1 Exposed: Security Flaws in China’s AI Model. KELA Cyber Threat Intelligence.

🔹 Unit 42. (2025, January 31). Recent Jailbreaks Demonstrate Emerging Threat to DeepSeek. Palo Alto Networks.

🔹 Wiz Research. (2025, January 29). Wiz Research Uncovers Exposed DeepSeek Database Leaking Sensitive Information, Including Chat History. Wiz Blog.

📌 Category 2: Jailbreaking Techniques & AI Attacks

🔹 Russinovich, M., Salem, A., & Eldan, R. (2024). Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack. Microsoft Research.

🔹 Unit 42. (2024, December 31). Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak LLMs by Misusing Their Evaluation Capability. Palo Alto Networks.

🔹 Unit 42. (2024, December 10). Deceptive Delight: Jailbreak LLMs Through Camouflage and Distraction. Palo Alto Networks.

📌 Category 3: AI Security & Model Governance

🔹 Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., … & Amodei, D. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228.

🔹 Ganguli, D., Lovitt, L., Kernion, J., Askell, A., Bai, Y., Kadavath, S., … & Clark, J. (2022). Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858.

🔹 Unit 42. (2024, December 10). How AI is Reshaping Emerging Threats and Cyber Defense: Unit 42 Predictions for 2024 and Beyond. Palo Alto Networks. ‌

📌 Category 4: AI Transparency & Responsible AI Development

🔹 Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P. S., … & Gabriel, I. (2021). Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359.

🔹 Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., … & Gebru, T. (2019, January). Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency (pp. 220–229).

🔹 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?🦜. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610–623).

📌 Category 5: AI Governance & Future Regulations

🔹 European Commission. (2023). The EU AI Act: A Risk-Based Approach to AI Governance.

🔹 National Institute of Standards and Technology (NIST). (2024). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce.

Disclaimers and Disclosures

This article combines the theoretical insights of leading researchers with practical examples, and offers my opinionated exploration of AI’s ethical dilemmas, and may not represent the views or claims of my present or past organizations and their products or my other associations.

Use of AI Assistance: In preparation for this article, AI assistance has been used for generating/ refining the images, and for styling/ linguistic enhancements of parts of content.

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