How to Secure AI Agents from Going Rogue

🚨 The Rise of Shadow AI: A Hidden Cybersecurity Risk in 2025 As AI tools become part of everyday work, a new challenge is quietly growing inside organizations — Shadow AI. Just like “shadow IT” in the past, shadow AI refers to the use of unapproved or unsupervised AI tools by employees — from ChatGPT clones to online model builders — without any formal oversight by IT or security teams. At first, it seems harmless: using an AI assistant to draft a report, analyze data, or write code faster. But behind that productivity boost lies a serious risk. 🧩 Why it matters: Sensitive data may be shared with external AI tools that store it permanently. Organizations lose control and visibility over where internal data is going. Compliance, privacy, and IP exposure risks skyrocket. Even well-intentioned employees can cause breaches unknowingly. 🧠 What’s driving it: Fast access to free AI tools online Slow corporate AI approval processes Lack of clear AI governance frameworks Curiosity and pressure to be more efficient 🛡 How to respond: Build internal policies for responsible AI use. Provide secure, approved AI tools for employees. Implement monitoring (CASB, DLP, network analytics) for AI data traffic. Focus on education, not punishment — awareness is the best firewall. In short: Shadow AI isn’t about blocking innovation — it’s about balancing innovation with governance. Organizations that recognize this early will turn a growing risk into a competitive advantage. --- Question for you: > Do you think companies should restrict public AI tools completely, or trust employees with guidelines? #CyberSecurity #AI #ShadowAI #InfoSec #DataProtection #ZeroTrust #TechTrends2025 ---

Generative AI can create perfect fake credentials. It can also detect them instantly. The same technology that breaks security is fixing it. This paradox is reshaping identity and access management across organizations. Generative AI transforms IAM through four key pillars: • Authentication with adaptive real-time adjustments • Authorization through intelligent policy generation • Audit processes that accelerate compliance checks • Administration via natural language interactions The benefits are substantial. Enhanced anomaly detection catches threats faster. Automated workflows reduce manual overhead. Identity threat simulation helps predict vulnerabilities. But challenges exist. Biased AI models create security gaps. Data quality issues compromise accuracy. Privacy concerns demand careful handling. The key insight? AI should augment human expertise, not replace it. 90% of organizations now use AI to strengthen defenses. Those balancing innovation with ethical considerations will unlock the full potential. The future of enterprise security isn't just about better technology. It's about smarter human-AI collaboration. How is your organization preparing for AI-powered identity management? #GenerativeAI #IdentityManagement #Cybersecurity 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gmtcn8Du

The Silent Risk in Data Protection: Shadow AI Every organization today is racing to leverage AI, and that’s exactly where the next data protection crisis is brewing. While companies invest millions in securing databases and encrypting storage, “shadow AI”, the unmonitored use of AI tools by employees quietly undermines it all. From uploading sensitive client information into chatbots to using generative tools for internal reports, confidential data is being exposed to third-party systems outside governance boundaries. Here’s the challenge: 🔍 You can’t protect data you don’t know is being shared. ⚙️ You can’t govern tools you don’t know are in use. 💬 You can’t build trust when employees don’t understand the risks. The solution isn’t just blocking AI tools , it’s building a data protection culture that scales: 1. Train teams to recognize data sensitivity, not just data categories. 2. Deploy policies that empower innovation safely. 3. Treat privacy as a design principle in every AI workflow. In 2025, the real measure of a mature organization isn’t how fast it adopts AI but how responsibly it does so, because in the age of intelligent machines, data protection is human intelligence. #DataProtection #AI #Privacy #Governance #CyberSecurity #DataEthics #Trust #Compliance

For years, our biggest worry was the malicious insider — the employee who copied files, leaked data, or walked out with IP on a USB stick. Now, there’s a new kind of insider, and it doesn’t take lunch breaks. Generative AI is the new insider threat. Every time an employee pastes source code, a client contract, or internal documentation into a chatbot, that data can be stored, learned from, or even retrieved by others through cleverly crafted prompts. It’s unintentional, but the effect is the same: sensitive data leaves the organization without detection. A few realities we need to face: AI tools are not “internal.” Even enterprise versions may retain metadata or training traces. DLP tools weren’t built for prompts. Most can’t detect when confidential info is shared with LLMs. (Though this is getting better) Prompt Injection = Social Engineering for Machines. Attackers can trick models into revealing data or executing actions outside their intended scope. So how do we stay ahead? Build AI governance that aligns with data classification — what can and can’t be shared. Deploy context-aware monitoring for AI interactions. Train teams to think: “Would I email this outside the company?” If not, don’t feed it to AI. AI isn’t evil — but it’s powerful, curious, and persistent. Treat it like a highly skilled employee with no concept of confidentiality. How is your organization balancing AI innovation with data protection? #CyberSecurity #AI #InsiderThreat #DataLossPrevention #CISO #AIsecurity #Governance

People First — Even in the Age of AI We’ve automated patching, hardened endpoints, and tightened access controls but have we trained people to think securely in the age of artificial intelligence? During a recent cybersecurity transformation programme I led, one truth became clearer than ever: no matter how advanced the technology, security always starts and ends with people. When we talk about Security Awareness Training, many still picture phishing simulations or annual refresher modules. But true awareness isn’t a checkbox activity it’s a cultural shift. It’s about helping people see security as a shared responsibility, not just an IT task. Now, as artificial intelligence becomes part of everyday work, that same principle applies and only the stakes are higher. Every day, employees experiment with generative AI tools, often without clear guidance. Sensitive data is uploaded into public models. Complex AI outputs are trusted without validation. The real challenge isn’t the technology itself, it’s the invisible gap between innovation and understanding. That’s why forward-thinking organisations are asking critical questions: - Do we have an AI Governance Policy that defines safe and ethical use? - Have we trained our people on the privacy and compliance implications of AI? - Who owns accountability for AI-related errors the user, the platform, or leadership? The organisations getting this right are embedding AI awareness and security training into their culture, just as they did years ago with phishing and insider threat initiatives. At the end of the day, every prompt, every query, every decision still starts with a person, and until we invest as much in judgment as we do in technology, our defences will always fall one decision short of resilient. How prepared is your organisation for the human side of AI adoption? Are your security programmes evolving fast enough to meet this new reality? #CyberSecurityAwareness #AIGovernance #PeopleFirstSecurity #HumanFirewall #ResponsibleAI #CyberResilience #LeadershipInTech #RiskManagement

Does ISO/IEC 42001 risk slowing down AI innovation, or is it the foundation for responsible operations? 🔒 Follow-up to my recent post on ISO/IEC 42001 and AI innovation While reflecting further, I wanted to highlight the cybersecurity dimension of AI risks that ISO/IEC 42001 is designed to address: ⚠️ AI Threats • Data Poisoning – injecting malicious data to compromise model integrity. • Model Stealing – unauthorized replication or use of models for illicit purposes. • Model Inversion – extracting sensitive information from model outputs. ✔️ How ISO/IEC 42001 helps • Strengthens resilience by preparing teams for effective incident response. • Identifies and evaluates AI risks and their impact on operations. • Embeds continuous improvement through the Plan–Do–Check–Act (PDCA) cycle. 📌 ISO/IEC 42001 vs ISO/IEC 27001 ISO/IEC 27001: Focuses on protecting sensitive information through confidentiality, integrity, and availability. ISO/IEC 42001: Focuses on managing AI systems responsibly with emphasis on ethics, transparency, and accountability. Both stress secure data and documentation, but for different scopes: 27001 for information security, 42001 for AI-specific risks. And importantly — ISO/IEC 42001 is not an extension of ISO/IEC 27001. Organizations can implement it independently, even if they haven’t fully implemented 27001. Combining both, however, creates a much stronger foundation for secure and trustworthy AI adoption. 💡 Takeaway: AI security isn’t optional — it’s essential for sustainable innovation. What’s your view — are organizations ready to tackle these new AI security challenges? #AI #Cybersecurity #ISO42001 #ISO27001 #ResponsibleAI #RiskManagement

Why Integrate Security into AI Development? 🔒🤖 In a world where artificial intelligence transforms industries, ignoring security can be costly. According to experts, embedding security practices from the start of the AI lifecycle is not optional, but essential to mitigate risks and foster responsible innovation. Key Risks in AI Development 🚨 - Adversarial Attacks: AI models are vulnerable to manipulations that alter their decisions, such as in computer vision systems where malicious data is injected to fool algorithms. 😈 - Data Poisoning: During training, contaminated data can bias results, leading to critical failures in applications like medical diagnostics or fraud detection. 🧪 - Exposure of Sensitive Data: Models trained with confidential information could inadvertently leak it, violating regulations like GDPR. 📊 - Scalability of Threats: As AI integrates into business operations, a breach can propagate quickly, affecting digital supply chains. ⚡ Benefits of Embedding Security from the Design 🛡️ - Proactive Prevention: Implementing frameworks like Secure AI Lifecycle ensures robustness tests and continuous audits, reducing vulnerabilities by 70% according to studies. 📈 - Compliance and Trust: Complying with global standards like NIST AI Risk Management Framework builds trust in stakeholders and accelerates adoption. ✅ - Secure Innovation: Teams that prioritize security develop more ethical and scalable AI, avoiding costly recalls and reputational damage. 🚀 - Interdisciplinary Collaboration: Involving cybersecurity experts from the planning phase fosters DevSecOps cultures adapted to AI. 🤝 The key is to adopt a holistic approach: from data collection to production deployment. Companies that do so not only protect their assets but lead in a responsible AI ecosystem. For more information visit: https://enigmasecurity.cl #AISecurity #Cybersecurity #AIDevelopment #DevSecOps #SecureInnovation Connect with me on LinkedIn to discuss more about AI security: https://lnkd.in/etNGUTDM 📅 2025-10-22T12:21:04.000Z 🔗Subscribe to the Membership: https://lnkd.in/eh_rNRyt

October is Cybersecurity Awareness Month! When using AI, it’s important to keep cybersecurity top of mind and it starts with thinking about your data: where is it stored in your workflow and where can it be shared safely. Watch to Learn AI with Michelle. The data risk isn’t about data actually appearing on a literal website. That’s a relatable example! It’s about: • Loss of control over where your data goes • Inability to delete it completely once shared • Unknown future uses as AI technology evolves • Legal and compliance ramifications • Competitive intelligence falling into wrong hands AI is everywhere now. It’s a power tool! But as the famous line goes - with great power comes great responsibility! Don’t forget to stop and think when you share data with any AI.

Emerging AI Risks: Are you aware of those? Beyond traditional cybersecurity concerns, AI introduces a new generation of risks that every business leader should be aware of: 🔹 Hallucinations – AI can generate false or misleading information, undermining trust and decision-making. 🔹 Harmful content – Deepfakes, offensive or legally non-compliant material can be produced at scale. 🔹 Model theft – The illegal copying of AI models erodes competitive advantage and exposes sensitive data. 🔹 Prompt injection – Attackers manipulate AI prompts to trigger unintended or malicious outputs. 🔹 Data poisoning – Tampering with training data introduces vulnerabilities and biases into models. 🔹 Excessive agency – Giving AI too much autonomy can lead to unintended actions and security breaches. 🔹 Regulatory pressure – Non-compliance with frameworks likeGDPR, or the EU AI Act can result in heavy fines and operational risk. AI brings innovation, but also responsibility. Understanding these risks is key to building secure, ethical, and resilient AI ecosystems. 👉 Which of these risks do you think will have the biggest impact in your industry? #AI #AISecurity #InformationSecurity #RiskManagement #ISO27001 #ISO42001