Demystifying AI & Quantum Risks

AI Risks Re-Exposed 🛡️ Why They’re Special and What to Watch Out For 🚨

In the future, autonomous AI could run critical systems like government services, public transport, & healthcare. That's the biggest risk—unpredictable failures could cascade massively.

AI isn't like traditional software. It learns & evolves on its own, making issues hard to predict/prevent. Key unique risks:

- Adversarial Attacks: Hackers tweak data to fool AI into disastrous errors.

- Data Dependency & Privacy: Massive datasets invite breaches, misuse, & violations.

- Bias Amplification: AI absorbs & spreads training data biases, causing discrimination.

- Black Box Problem: We often can't explain AI decisions, killing trust & accountability.

🚨 Implications 

- Individual: Biased algos deny jobs/loans unfairly.

- Organizational: Security gaps lead to breaches & reputational damage.

- Ecosystem: Algo trading crashes markets; automated logistics grinds supply chains to a halt.

Unlike IT bugs (fixable with patches), AI risks hit the "brain" of the system—need specialized defenses.

Quantum Risks

Future wise, the Quantum Ripple Effect amplifies AI threats when quantum computing intersects with autonomous systems: Quantum processors could shatter current encryption, exposing vast AI training datasets to breaches and enabling adversaries to manipulate models at scale. 

A slippery slope ⚠️ or a snowball effect that has increased the risks: increasing bias becomes discrimination, hostile attacks become catastrophic, and black box failures in critical sectors like healthcare or the Internet cause ecological-wide failures.

As AI evolves toward quantum integration, unprepared systems face exponential vulnerabilities, demanding urgent quantum-resistant protocols to prevent ripple-like global disruptions.

Therefore, Let's try to build safely AI - according to regulations like

- NIST AI RMF, - OECD or - EU AI act.

Let's prepare to mitigate risks in AI while strengthening the protections and encryptions threatened by quantum computing, to strengthen against these future risks! ☠️

Would you like to share a thoughts 💭 ? 

https://youtu.be/OufwfdcxrNk?si=k-gEjE-4JGtAoN-Q

#AIRisks #xAI #QuantumRisks #Quantum

You find it's a tight regulation for a new artificial intelligence (AI) - No problem

 Building a new AI system? Don't wait for regulation to surprise you 🚀

The world is moving towards tight regulation of artificial intelligence, but the average entrepreneur or product manager finds himself facing a maze: the European EU AI Act, the US NIST standards and the OECD principles.

What's the difference and how do you stay relevant?

Basically, everyone agrees on the "what": human rights, fairness, transparency and safety. But the "how" is completely different: 🔹 OECD: Voluntary value framework ("Soft Law"). The moral compass that everyone started with. 🔹 EU: Mandatory regulation ("Hard Law") with teeth, risk classification and heavy fines. 🔹 US: A combination of voluntary guidelines and sectoral regulation (health, finance).

🗯️ My tip: Start with the OECD, but aim for UFA 🎯 If you align yourself with the OECD principles, you are already on the right track. But to be truly market-ready Globally, it is worth adopting the Unified Framework Approach (UFA): adopting the most stringent standard (usually the European one) as the house standard. This saves expensive "corrections" afterwards.

Quick checklist for alignment (OECD Alignment):

✅ Defining uses and risks: Who are the users? What are the prohibited/sensitive uses? ✅ Data and model: Documenting the sources of information, legal basis (Consent) and separation between training and testing sets. ✅ Pre-launch testing: Accuracy metrics, fairness tests (Bias testing) and robustness. ✅ Transparency and accountability: Clearly wording for the user when he is facing AI, how to challenge a result and who is responsible in the organization. ✅ Continuous monitoring: A channel for reporting failures and setting time points for re-testing (quarterly/semi-annually).

Bottom line: Regulatory compliance is not just a legal "headache" - it is a tool for building trust with your customers. Company A company that documents and manages risks in advance is an easier company to sell and invest in.

AI Cybersecurity Foundations

 The document 📌 "AI Cyber ​​​​Security Lays the Foundation"

is a concise, focused and practical guide that aims to lay the foundation for understanding cybersecurity in artificial intelligence (AI) systems. It is aimed at a professional audience – such as security officers (CISOs), developers, organizations and research – and provides a practical descriptive framework to address the unique security challenges of AI, such as generative models (LLM) and autonomous agents (Agentic AI).

To read or watch 👁️ click on the link 👈🏻 https://lnkd.in/dUf6-eek ✋🏻

#AI_Security_Security #AI_Security_Foundation