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8 contributions to AI Cyber Compliance Hub
7d •
The Hidden Risks of AI in Cybersecurity
Artificial Intelligence (AI) is rapidly transforming the cybersecurity landscape — both as a powerful defense tool and a potential vulnerability. 🔍 The Double-Edged Sword of AI AI enhances real-time threat detection, automates incident responses, and helps anticipate attacks using predictive modeling. However, the same tech is being weaponized by bad actors. 🚨 Top Risks to Watch Out For: - Adversarial Attacks – AI models can be manipulated by poisoned data or prompts. - Data Privacy Leaks – Poorly secured AI systems may unintentionally expose sensitive data. - Bias & Compliance Violations – Improperly trained AI can lead to biased outcomes, violating GDPR, CPRA, or the AI Act. 🧰 Action Steps: 1. Perform regular AI model audits 2. Monitor inputs/outputs for adversarial behavior 3. Use compliant data sets during training 4. Bottom Line: AI in cyber is a must-have — but only with tight compliance and oversight.
0 likes • 7d
OWASP got AI security guidelines.
0 likes • 5d
@Ashraf Mohmed 100%
7d •
Securing the Future: How Governance Shapes AI Cybersecurity
As artificial intelligence rapidly advances, so do the complexities of securing it. A recent deep dive from the R Street Institute explores how governance, risk, and compliance must evolve to manage the intersection of AI and cybersecurity. This post unpacks their findings and what it means for professionals working at the frontlines of cyber compliance. 🧠 1. Securing the Foundations of AI One of the key takeaways is the urgent need to secure AI infrastructure, including the data, models, and networks behind AI systems. While AI can detect threats faster than traditional methods, it also introduces new vulnerabilities — especially if development practices lack built-in security. R Street highlights the lack of universal auditing standards as a major gap. Without reliable metrics, it's hard to know if AI systems are truly secure. They recommend public and private investment into standardized metrics, risk frameworks, and "red teaming" practices — such as those being developed by the U.S. AI Safety Institute. 🧭 2. Promoting Responsible AI Use Governance isn’t just about stopping bad actors — it’s about setting the stage for responsible use. R Street warns that vague definitions (e.g., what constitutes “open-source AI”) and outdated legacy systems are stalling progress. Their advice? - Develop clear definitions and security standards - Modernize systems to support evolving AI needs - Anticipate emerging risks like cloud-based AI vulnerabilities Responsible AI isn’t just about ethics — it's about functionality and long-term trust. 👥 3. Bridging the Cyber Skills Gap Another major governance concern is the lack of trained talent. AI is moving fast, but the cybersecurity workforce isn’t always ready to keep up. R Street points out the value of AI-powered simulations and adaptive learning to train professionals. Still, small-to-medium businesses (SMBs) often lack resources to invest in this education, creating a divide in readiness. Their solution? Targeted training programs focused on:
0 likes • 7d
AI is the future for cyber
0 likes • 5d
@Ashraf Mohmed indeed
7d •
AI & Cybersecurity — Balancing Risks and Rewards
As we enter a new era of automation and intelligent systems, AI is transforming how we approach cybersecurity — not just as a tool for defense, but also as a potential attack surface. The World Economic Forum’s 2025 report, “Artificial Intelligence and Cybersecurity: Balancing Risks and Rewards,” emphasizes the importance of understanding both sides of the equation: the tremendous value AI brings and the emerging vulnerabilities it introduces. 🔍 The Opportunity Side: AI as a Security Multiplier AI is accelerating threat detection, streamlining incident response, and automating routine tasks — making cybersecurity more efficient and adaptive. Key areas of opportunity: - Predictive Threat Detection – ML models help detect patterns and anomalies before attacks occur. - Automated Response – AI-driven SOAR platforms can contain threats in real-time. - Behavioral Analytics – AI monitors user behavior to detect insider threats and compromised accounts. Organizations deploying these technologies report faster detection, fewer false positives, and reduced manual workload for cyber teams. ⚠️ The Risk Side: AI as a Double-Edged Sword While AI strengthens defenses, it also introduces new attack vectors: - Adversarial Attacks – Hackers manipulate inputs to mislead AI models. - Data Poisoning – Malicious actors inject corrupted data during training phases. - Model Theft & Inversion – Bad actors extract proprietary models or reverse-engineer inputs to reveal sensitive information. The WEF warns that trust in AI is fragile — and must be earned through governance, transparency, and continuous testing. 🏛️ Governance & Global Policy A major focus of the WEF report is the lack of standardized global AI cyber policies. Currently, most regulations (like GDPR, AI Act, CCPA) handle privacy and risk after deployment. The report advocates for: - Pre-market risk assessments - Mandatory model auditing - International cooperation on cyber-AI norms Without coordinated global efforts, the regulatory patchwork will leave critical gaps that sophisticated actors can exploit.
0 likes • 7d
Yeah let's do it.
0 likes • 5d
@Ashraf Mohmed indeed
6d •
👋 NEW MEMBERS : Welcome to AI Cyber Compliance Hub!
Hey everyone — Lets welcome the new members @Stephen Nwaokolo @Lucas Edmonds @K H @Bassam Khatib @Ahmed Amad to the AI Cyber Compliance Hub! 🎉We’re excited to have you here. This is a place for professionals, learners, and curious minds to explore the fast-moving world of AI, cybersecurity, and compliance — together. Whether you're just getting started or deep in the field, you’re in the right place. As a 1st step Jump into this https://www.skool.com/aicybersecurity/our-vision?p=35f31bb6
1 like • 6d
@K H you're welcome
2 likes • 6d
@Lucas Edmonds you are welcome. Happy to have you here.
7d •
OUR VISION 🔐
[IMPORTANT READ] Wow! Thanks for joining AI Cyber Compliance Hub! 🚀 Our goal? Bring together 100+ security-minded professionals and make this the go-to community for compliance insights and growth. This Skool group is your launchpad to: - ✅ Build bulletproof compliance frameworks - 🧠 Learn from pros in ISO 27001 , SOC 2, GDPR & more - 📈 Share real-world risk mitigation wins - 🛠️ Access templates, guides, and system walkthroughs Together, we’re building a high-trust network that helps each other grow. 🔥 First Mission: Streamline your compliance operations and protect your org with confidence 💥 Swap war stories from audits, build a personal brand in the GRC world, and get better—together. FEW THINGS TO DO: 1. Introduce yourself! 📣 2. Tell us what area of compliance you work in 💬 Let’s scale your impact and secure the future—together 💪 – K J
0 likes • 6d
@Lucas Edmonds Hey Lucas, great point — and you’re absolutely right to raise it here 👏 This is exactly the kind of discussion we need in AI Cyber Compliance. When people talk about “owning your data,” it’s not just a philosophical issue anymore — it’s a compliance, cybersecurity, and even identity-rights issue. To break it down simply: - Data Ownership: Most platforms’ Terms of Service give them broad usage rights to anything you upload (text, images, voice). You technically retain ownership, but you grant them a license to use, distribute, or even train AI models with it. - Identity Protection: We’re now entering a stage where your likeness, voice, and even DNA can be replicated by AI. That’s where emerging frameworks like AI Governance (ISO/IEC 42001) and AI Risk Management (NIST AI RMF) come into play — they aim to protect individuals and organizations from unauthorized model training, data leaks, and synthetic identity misuse. - Legal Trend: Trademarking your face or voice is becoming a form of “digital identity compliance.” It’s not mainstream yet, but it’s being discussed by privacy regulators and digital rights groups — especially as “deepfake” regulations tighten in the EU AI Act and upcoming U.S. AI policy drafts. So your question isn’t off-topic at all — it’s actually the future of AI compliance and cyber law in our context here, this touches AI data governance, digital identity protection, and ethical model training — all key themes we’ll explore more in this hub. If anyone here has worked on policies for AI data ownership, model consent, or likeness protection, please jump in — would love to build a mini knowledge base around that topic. 🧭 Optional References You Can Mention: - NIST AI Risk Management Framework (AI RMF 1.0) – defines controls for data provenance, integrity, and transparency. - ISO/IEC 42001:2023 – the world’s first standard for AI management systems, covering responsible use and compliance governance. - EU AI Act & GDPR (2024–2025 updates) – introducing obligations around consent, data reuse, and model explainability. - World Economic Forum: “AI Identity and Deepfake Governance” (2024) – outlines trends in protecting human likeness and voice.
0 likes • 6d
@Bassam Khatib welcome to the group
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