The invisible AI estate: A new threat to enterprise security. Introducing VANTAGE, a framework for managing AI security posture.

🚀 Defending Against Unauthorized Usage in AI Systems AI security goes far beyond access control or protecting APIs. To build trustworthy and resilient AI systems, we need a multi-layered defense strategy that covers both traditional security and AI-specific risks. I designed a 10-layer AI Security Pyramid, combining classical practices with new dimensions for the AI era: 🔹 1. Access Controls & Authentication – Strong access controls and MFA. 🔹 2. API Security – Rate limiting and proper authentication. 🔹 3. Monitor Usage Patterns – Anomaly detection and unusual usage monitoring. 🔹 4. Regular Security Audits – Frequent pentesting and security assessments. 🔹 5. Data Security & Privacy – Encryption, anonymization, and data lineage. 🔹 6. Model Security & Integrity – Hardening against extraction, adversarial ML, and model signing. 🔹 7. Governance & Policy Enforcement – Advanced RBAC, compliance, and acceptable use policies. 🔹 8. Threat Intelligence & Incident Response for AI – AI honeypots, threat modeling, and IR playbooks. 🔹 9. Continuous Security Automation – CI/CD integration, adversarial testing, policy-as-code. 🔹 10. Supply Chain Security for AI – Verify ML/DL dependencies, MBOM, and dataset/model integrity. 📌 Securing AI requires thinking beyond traditional infrastructure. It’s not only about data and systems — we must also secure models, pipelines, and the AI supply chain. 👉 Which of these layers do you think is the most critical for organizations adopting AI today? #AISecurity #Cybersecurity #MLOps #AI #SupplyChainSecurity #Governance #AITrust

I’ve been speaking with a few information, security, and audit teams this week about how they’re approaching AI adoption - and one theme keeps coming up: "We’re moving fast on innovation and implementing AI, but we're worried about our security keeping pace" A few themes coming up: 🔹 AI sprawl / shadow AI Teams experiment with agents, AI tools, and builders across the business, often without central oversight - every workflow widens the attack surface. 🔹 Business continuity When a single model provider goes down (both Anthropic & OpenAI are amazing models, but have both been down in the last week), critical workflows pause. Resilience means routing to alternate models and monitoring for latency & drift. 🔹 Testing and governance Red-teaming (single & multi-turn) is helping organisations spot jailbreaks, leakage, and model drift before they become incidents. The companies getting this right treat AI security as part of their foundation, not a bolt-on: 🧩 Guardrails against injection attacks 🧩 Monitoring for drift and data leakage 🧩 Fallbacks across models If your organisation is scaling AI, now’s the moment to make security part of the design, not an afterthought 💡 How are you thinking about protecting LLMs, and agents as usage grows? #AIsecurity #promptinjection #redteaming #AIgovernance Airia - Enterprise AI Simplified

Scaling AI requires a strong foundation — built on four essential pillars: 1️⃣ AI Governance – drive responsible and transparent AI adoption 2️⃣ AI Security – protect models, data, and insights from evolving threats 3️⃣ Data Governance – ensure quality, compliance, and control 4️⃣ Data Security – safeguard sensitive information at every stage Discover how a unified framework brings governance and security together to enable trust, accountability, and performance across the entire AI lifecycle: https://ibm.biz/Bdeas2

This is so true, and what’s more alarming is that most organizations are not even assessing these risks in their environment. I think this is exactly where ISO 42001 will fit in the coming years, or perhaps a newer version of 27001, since a lot has changed since November 2022 (the launch of ChatGPT). This will bring better governance and a more structured approach to the usage of AI.

🚨 Shadow AI usage just surged 50% in 90 days and most of it is invisible to security and compliance teams. Kiteworks UK’ latest insights + the Netskope report reveal a disturbing reality: 📈 8.2 GB of corporate data is uploaded monthly often IP, credentials, source code, and regulated data. 🤖 1,550+ GenAI tools are in active use many unapproved, unmanaged, and untracked. 🧠 Employees are building AI agents that access production data, modify code, and execute commands without security review. 💻 34% run AI infrastructure on personal machines with zero built-in authentication. 📊 60% of AI use is completely ungoverned. Traditional governance frameworks can’t keep up. Slow, policy-heavy approaches are obsolete. ✅ Visibility is your first defense. ✅ Sanctioned enablement beats shadow experimentation. ✅ Private Data Networks enforce governance before data reaches the model. Kiteworks UK is at the forefront of securing AI workflows with real-time control, granular visibility, and zero-trust AI data protection. The AI genie isn’t going back in the bottle but you can build a secure, compliant environment around it. #ShadowAI #AIgovernance #GenAI #DataSecurity #Cybersecurity #Kiteworks #Infosec #AICompliance #ZeroTrust #DataGovernance #PrivateDataNetwork #kiteworks

In our May 2025 AI Agent Survey, 53% of US businesses deploying AI agents report using them in IT and cybersecurity. But not every organization is moving fast enough. And the stakes are high. According to our 2025 Global AI Jobs Barometer, companies leveraging AI see revenue growing three times faster per worker, underscoring the risk for CIOs that haven’t yet begun to empower the enterprise with AI agents. #Agentic #AI #Operatingmodel https://lnkd.in/gDDexYBd

In our May 2025 AI Agent Survey, 53% of US businesses deploying AI agents report using them in IT and cybersecurity. But not every organization is moving fast enough. And the stakes are high. According to our 2025 Global AI Jobs Barometer, companies leveraging AI see revenue growing three times faster per worker, underscoring the risk for CIOs that haven’t yet begun to empower the enterprise with AI agents. #Agentic #AI #Operatingmodel https://lnkd.in/gDDexYBd

According to the ASIS 2025 Security Trends Report, 57% of security professionals have tried at least one AI tool. Yet adoption remains uneven—many still hesitate to integrate AI into their workflow. For security practitioners in high-threat environments, this matters. AI isn’t about replacing professional judgment—it’s about strengthening the risk management process: 1. Faster contextual scanning of threats 2. Early detection of protest movements or access barriers 3. Testing mitigation strategies through scenario modeling 4. Turning incident reports into actionable lessons learned Security is ultimately about enabling operations. I think, those who adopt AI early will be better equipped to think strategically, act proactively, and deliver securely. Do you agree?

Shadow AI is no longer an edge case, it’s a growing security risk. When generative AI tools are adopted without IT oversight, companies face increased risks of data leaks, compliance violations, and exposure of sensitive content. This silent trend is reshaping enterprise security posture, often without decision-makers even noticing. Here’s what Shadow AI is, why it matters, and how to start addressing it: https://lnkd.in/dAYSNSVn #ShadowAI #AICompliance #AISecurity #EnterpriseAI

A dark side of AI is already here in nearly every company. SentinelOne warns that employees using unsanctioned AI tools is causing data leaks, compliance violations, and serious blind spots. “The need for oversight… is just an imperative.” – SentinelOne CEO This is Shadow AI — invisible, unmanaged, and dangerous. Full story: https://lnkd.in/gAczDpMM What guardrails should businesses implement for AI usage?