AI Transformation

  Securing MCP Servers in Agentic AI The Model Context Protocol (MCP) lets large language models interact with external tools, turning them from passive text generators into active agents. The promise is huge—agents can fetch data, trigger workflows and orchestrate complex tasks. However, each MCP connection creates a bridge between untrusted model outputs and sensitive systems: a single weak point (a poisoned prompt, an over‑broad permission or an unverified tool) can be abused. Despite the availability of an MCP specification, most reference servers implement only the basic transport and authorization flows. They omit crucial hardening such as verification of incoming requests, secure session identifiers and tooling policies. As we deploy agentic AI more widely, we need a security layer that goes beyond protocol compliance. This article distills the major risks facing MCP deployments, highlights existing best practices from the protocol specification, and proposes a co...

  Securing Agentic AI: Why Persona Definitions Are the Next Frontier Agentic AI is here. These aren’t static chatbots—they’re autonomous systems that call tools, make decisions, and sometimes even negotiate on our behalf. But autonomy comes with risk: prompt injection, tool misuse, memory poisoning, impersonation, and uncontrolled escalation. The default approach so far? Bolt on security later—filters, firewalls, audits. But that’s not enough. My view: security must be embedded into the agent’s very definition . The Case for Security-Embedded Personas Every agent begins with a persona—a role definition describing its purpose. We usually think of personas as functional (“retriever,” “planner,” “executor”). But in a world of autonomous systems, a persona should also be a security boundary . Much like an employee’s job description defines what they can and cannot do, an agent’s persona must encode authority, limitations, and auditability. Elements of a Secure Agent Persona 1...

 For decades, computer science education has focused on how much knowledge you can acquire—learning programming languages, memorizing algorithms, and understanding data structures. But with Generative AI changing the landscape, the focus is no longer just on what you know; it’s about how fast you can access, apply, and adapt knowledge to solve problems. The world is moving at an unprecedented pace, and those who embrace AI as a collaborator rather than resist it will lead the future. Here’s how Generative AI is transforming computer science education and what it means for the next generation of thinkers, builders, and problem-solvers. 1. It’s No Longer About Memorization—It’s About Adaptation Imagine you’re solving a complex problem—previously, you might have spent hours reading textbooks, researching online, or asking professors for help. Now, AI can generate answers in seconds. It can explain algorithms, write code snippets, and debug programs. It can analyze large datasets, sugg...