Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

I Faked a Receipt with ChatGPT

Generative AI can produce realistic taxi receipts, complete with stains and wear, which blend into digital expense workflows that expect only a quick photo upload. As more organisations move to app based reimbursement, synthetic documents slip through unless controls, audits and behavioural checks keep pace with these tools. ⸻ For more information about us or if you have any questions you would like us to discuss email podcast@razorthorn.com. We give our clients a personalised, integrated approach to information security, driven by our belief in quality and discretion..

Cato AI Security: Is Your Security Stack Built for How AI Works?

AI adoption is accelerating across enterprises — often faster than security teams can respond. Employees are using AI tools and copilots across SaaS apps and workflows, creating new exposure around sensitive data, shadow AI, and attack surfaces that traditional tools weren't built to see. This video breaks down the four AI security challenges every enterprise is facing, where existing controls fall short, and how Cato AI Security gives you visibility, guardrails, and enforcement across the AI your employees use, the applications you build, and the agents acting on your behalf.

Scale CMMC services without delivery chaos using ComplianceAide and Acronis integration

By Randy Blasik, Founder, ComplianceAide The good news for managed service providers (MSPs) supporting defense contractors is that demand for Cybersecurity Maturity Model Certification (CMMC) and NIST 800-171 readiness services is surging. The downside, unfortunately, is that many MSPs have discovered that delivering compliance engagements at scale can be difficult and complex.

Cloud-Native Security for AI Workloads: Why It Matters and What's Changed

You’ve been securing Kubernetes workloads for years. Your CSPM is running, your CNAPP is configured, your team knows how to triage container alerts. Then an AI agent lands in your cluster — maybe from the data science team, maybe from a vendor integration, maybe from a tool you didn’t even know was running. Within a week, it’s making API calls nobody planned, accessing data stores that aren’t in the architecture diagram, and executing code it generated itself.

AI Workload Security Tools: Runtime vs. Declarative Compared

You’re forty-five minutes into a vendor demo for AI workload security. The dashboard looks polished—posture scores, misconfiguration findings, vulnerability counts, all tagged with an “AI workload” label that wasn’t there last quarter. You ask the obvious question: “Show me how this detects a prompt injection attack on our production agent.” Long pause. The SE pulls up a generic process anomaly rule.

Why Generic Container Alerts Miss AI-Specific Threats

It’s 2:47 AM and your SOC dashboard lights up. Six alerts fire across three hours from a single Kubernetes cluster: an outbound HTTP fetch to an unfamiliar domain, a tool invocation inside a customer support agent, an API call to an internal service the agent has never contacted, a service account token read, a file write to a model artifact directory, and an outbound data transfer that looks like normal API usage.

AI Workload Security for Financial Services: What CISOs Need to Know

When your SOC alerts on “suspicious AI activity” in a production trading system, your response team faces a question that didn’t exist two years ago: can you explain to regulators exactly which function processed the malicious prompt, which internal tool it called, and how customer data ended up leaving your environment?

How a Fortune 50 Company Deployed Agentic AI at Scale Without Losing Control of Their Data

In late 2025, a Fortune 50 enterprise decided to deploy autonomous AI agents across core business operations. Customer support that could reason through complex issues. Supply chain systems that could adapt in real time. Product managers with AI assistants pulling insights from dozens of data sources simultaneously. The capabilities that made the agents useful also introduced a problem nobody had a clean answer for. These weren’t chatbots locked inside a single application.

Why Synthetic Data for AI Fails in Production

Synthetic data has been fine for testing software for decades. Traditional apps follow rules. You check inputs, check outputs, file a bug when something breaks. AI is different. AI gets deployed into the situations where the rules aren’t clear and context is everything. The edge cases aren’t exceptions. They’re the whole point. That changes what your test data needs to look like.

Are AI Security Tools the New EDR? Attackers Are Treating Them That Way

AI security tools are no longer just defensive layers. They are high value targets being studied, fingerprinted, and bypassed much like traditional endpoint detection and response (EDR) platforms and antivirus solutions were in their early days. The speed and scale at which these tools are being deployed makes reactive defense increasingly unsustainable.