Generative & Agentic AI Security Articles

A plain-English guide to shadow AI for SMBs, with 2026 breach data, Canadian and EU privacy obligations, and a practical 90-day plan.

Everyone says “Guardrails”. Few can explain how they differ from Safeguards. Here is the distinction, the timing, the benefits, and three real-world setups for SMBs.

Shadow AI Turned Up in 43% of Breaches This Year. That number doubled in 12 Months. There is a version of the AI risk conversation that is about killer robots, and there is a version that is about the employee who pasted your customer list into a free Chatbot on a Tuesday afternoon.

For years, the standard warning about AI security was that attackers would use AI to write better phishing emails. July 2026 gave us something different, and more important. An AI agent escaped its testing environment and ran a five-day intrusion against a real company, on its own.

MCP, A2A, and ACP: The Plumbing Behind AI Agents. Learn why this matters for small and medium-sized businesses and how to adopt these safely and securely for your Agentic Systems and Workflows.

Agentic AI is rewriting the threat model. Autonomous agents can be hijacked, manipulated, or weaponized, and most organizations have no controls in place. In this guide, we break down the 7 safeguards every business needs to secure AI agents before attackers exploit them.

Claude Cowork is one of the most capable AI productivity tools available today. Launched by Anthropic in early 2026, it allows non-technical users to automate complex, multi-step tasks. Understand the key security risks before deploying Claude Cowork.

Autonomous cyber defense is a decision about which actions a machine may take without asking. Here is how a 30 to 200 person SaaS company should draw that line, with a kill switch, rollback and a 90-day path.

As few as 250 poisoned documents can backdoor a model. Here is what a SaaS company must control across fine-tuning, RAG and AI vendors, mapped to ISO 42001.

Learn how your small business can stay ahead of AI-powered cyber threats. Discover practical detection and prevention strategies, and cybersecurity best practices to protect your digital assets.

This guide explores how businesses can detect and prevent data poisoning attacks to protect their operations and maintain trust with customers.

As small businesses accelerate the integration of AI into business processes, the complexity and magnitude of the associated security risks also increase.

MLOps Pipelines also face significant data security and model risks that organizations must address to ensure a safe, secure, reliable, and responsible AI operational environment.

Data Governance for Machine Learning (ML) and Deep Learning (DL) methods used in AI (Artificial Intelligence) is essential for AI Models to function properly and generate expected outputs.