Let’s be real for a second: Enterprise AI adoption is moving faster than our ability to secure it.

As a founder in the Agentic AI space, I see this tension every day. Engineering teams are racing to deploy open-weight models and build autonomous agents, while CISOs are staring at a massive “blind spot” of Shadow AI and private stacks.

The old playbook—blocking everything—is dead. It started with blocking ChatGPT three years ago, but the needle has moved. Now, the goal is safe enablement at scale. But here is the hard truth: Traditional network security cannot protect Generative AI.

If you are trying to balance rapid adoption with deep security, you need to rip up the old map. Based on recent industry conversations, here are the four pillars of the modern AI security stack.

Right now, point solutions are creating visibility gaps across data, identity, and runtime activity. You might know who is using AI, but do you know what models they are calling or where that data is flowing?

We need to move toward a total AI Asset Management approach. This means maintaining a live “AI Bill of Materials” that tracks every agent, every foundational model (whether on AWS Bedrock, Azure, or GCP), and—crucially—their interdependencies and data access permissions.

For the last decade, we relied on static signatures and regular expressions (Regex) to catch bad actors. That doesn’t work with LLMs.

To catch things like prompt injection, PII leakage, or hallucinations, you need context, not just pattern matching. This requires a shift from CPU-based security to GPU-based security. We need security engines that “read” the conversation in real-time to understand intent, distinguishing between a harmless query and a malicious attempt to exfiltrate data.

We talk a lot about “shifting left,” but in AI, the development cycle is too fast for manual pen-testing.

The emerging best practice is automated Red Teaming that feeds directly into runtime defense. When your red team (or automated tool) finds a vulnerability in the development phase, it shouldn’t just sit in a PDF report. It should trigger “probe-to-rails” automation—instantly updating your runtime guardrails to block that specific attack vector in production.

This connects the discovery of risk directly to the prevention of risk.

We are currently at the cusp of the Agentic AI revolution. By 2026, we expect massive adoption of autonomous agents performing tasks on behalf of humans.

But this creates a massive authorization problem. When an AI agent talks to another agent or an MCP (Model Context Protocol) server, who verifies the identity?.

We must extend Zero Trust architectures to non-human entities. We need identity frameworks where an agent inherits the permissions of its human user (digital twin) or has a distinct, verified machine identity for autonomous tasks.

We are building high-speed trains, but we can’t forget the brakes.

The goal isn’t to slow down—it’s to build a platform where security is “designed in” from the start. By consolidating visibility and moving from network-based controls to context-based Zero Trust, we can close the trust gap.

About the Author

Rejith Krishnan

Rejith Krishnan is the Founder and CEO of lowtouch.ai, a platform dedicated to empowering enterprises with private, no-code AI agents. With expertise in Site Reliability Engineering (SRE), Kubernetes, and AI systems architecture, he is passionate about simplifying the adoption of AI-driven automation to transform business operations.

Rejith specializes in deploying Large Language Models (LLMs) and building intelligent agents that automate workflows, enhance customer experiences, and optimize IT processes, all while ensuring data privacy and security. His mission is to help businesses unlock the full potential of enterprise AI with seamless, scalable, and secure solutions that fit their unique needs.