Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI agents are being rapidly embedded into the SaaS ecosystem to streamline operations, trigger complex workflows, and interact with sensitive data and systems. From automating calendar updates to executing code and accessing cloud data stores, they are becoming integral to business processes. But with this integration comes risk. AI agents are often quickly deployed across SaaS environments by employees, without centralized tools to govern them.

Every demo promises that the AI is smarter, faster, and more reliable than humans.

PwC recently did an AI agent survey where they found the following: This all sounds great, right? For many reasons it is, but agentic AI creates a challenge of visibility for organizations into how AI agents are communicating with each other and external third-party vendors. Imagine a multitude of AI agents autonomously exchanging data across a complex mesh of third-party vendors and applications.

Vibe coding is a new development paradigm where large language models (LLMs) generate code from natural language prompts. The developer’s role shifts from writing every line to guiding the model toward a desired output—iterating through prompts, feedback, and high-level direction.

At Tines, our mission is to power the world’s most important workflows. That extends to how you implement and control AI across your business. We're excited to introduce the ability to build Model Context Protocol (MCP) servers directly on your Tines storyboard.

Large language models (LLMs) are transforming enterprise operations, but their growing use introduces a critical security challenge: securing how they access sensitive data and integrate with existing tools. This is where Model Context Protocol (MCP) servers become a vital, yet often overlooked, part of AI security. These servers act as the crucial link, enabling LLMs to securely connect with diverse data sources and tools, significantly expanding attack surfaces that demand our immediate attention.

Any organization that’s undergone a security transformation knows the promise of zero trust network access (ZTNA): secure, least-privilege access to private applications, anywhere, on any device. But turning that promise into operational reality is often far from simple. Between fragmented tools, complex configurations, and sprawling environments, implementing ZTNA can quickly become a manual, time-consuming, and error-prone process.