Agentic AI Security: The Current Market Landscape

A Founder’s Perspective on How the Landscape Is Forming

Over the last twelve months, enterprises have been experimenting with Agentic AI and building autonomous agents, MCP servers, complex Agentic AI Systems for business and customer uses cases. Security teams have been aware of this new reality: the attack surface that AI expose now. It has shifted from models and prompts to agent behaviors, tools, MCPs, and downstream actions those agents can perform.

If there’s one question I hear in nearly every CISO conversation today, it’s this:

“What exactly is the Agentic AI Security stack supposed to look like?”

The space is moving fast, terminology is inconsistent, and most enterprises don’t have a shared mental model for where different capabilities belong. Inside the organization, different teams are fighting to own “the AI project,” while dozens of AI agents, apps, and MCP experiments pop up across departments with no unified structure.

This article is my attempt to map the landscape as it stands today, as a founder working with enterprises using AI, MCPs, agents, and Agentic AI workflows.

The 5-Layer Agentic AI Security Stack

As the security architecture for Agentic AI is consolidating around five essential layers. These layers follow the lifecycle of an agent: from what it is, to what it can do, to how it behaves, to how it’s tested.

1. Agentic AI Discovery and Governance (Agent & MCP Control Plane)

This is the source of truth for the entire agentic ecosystem inside an enterprise. Governance platforms answer questions that no traditional AppSec or AI security tool can address yet:

• What agents exist across the company?

• Which MCP servers are running, where, and managed by whom?

• What tools, actions, and resources are exposed to these agents?

• What data flows between agents, tools, apps, and APIs?

• Which teams actually own these systems?

A governance layer solves this by providing automated discovery of MCP servers, agents, tools, and resources; mapping out data flows; and surfacing posture issues, misconfigurations, and risky permissions. It becomes the system that tells you not just what an agent is, but what it could do, and under what conditions. At its core, governance offers a way to define centralized policies, essentially, “Which agent is allowed to call which tool, with what privileges?”

Agentic AI Discovery and Governance is playing the same foundational role CSPM played when cloud infrastructure began scaling faster than security and governance could keep up.

And the timing couldn’t be more critical. Enterprises are adopting agents in a completely bottom-up, fragmented way. Product teams are spinning up internal tools agents. Customer support experiments with their own AI workflows. Developers are standing up homegrown MCP servers. Security teams often don’t know half of what’s running. Meanwhile, AI labs inside these companies are experimenting at a pace traditional governance models can’t track

The result is predictable: no centralized inventory, no visibility. That’s the real risk—intentional or not, agents can quickly overstep their boundaries simply because no one has mapped the boundaries in the first place. At the same time, MCP is rapidly becoming the default layer for agentic systems. It’s spreading across organizations faster than ever.

This is why governance is emerging as the highest-leverage layer of the Agentic AI Security stack.

Without governance, enterprises lose visibility and ownership of their AI footprint. That creates blind spots, unclear ownership, and real risk across the business.

2. Identity of Agents (Non-Human Identity Security)

As autonomous agents start reading data, calling tools, and triggering workflows, they effectively become non-human identities inside the enterprise. But most organizations still treat them like temporary features instead of entities with credentials, tokens, and permissions that must be governed.

Identity focuses on what access these agents have - their secrets, tokens, privilege levels, and how those permissions drift over time. It also identifies over-privileged and unmanaged agents that teams create outside formal review.

A single leaked token or overly broad permission can give an agent access to systems far beyond its intent.

And shadow agents operate with rights no one approved, creating silent but high-impact risks. Without strong identity controls, enterprises end up with agents holding excessive permissions, invisible privilege drift, and shadow workflows that lead to real operational and regulatory exposure.

3. Runtime (Guardrails, Traffic, Threat Detection and Blocking)

Runtime is where agent behavior becomes real. It’s the layer that checks every prompt, response, and tool call as it happens and stops anything unsafe. This is where enterprises enforce guardrails against prompt injection, jailbreaks, unsafe tool usage, sensitive data leakage, and harmful chain-of-action patterns.

Runtime controls sit directly in the request/response path at the proxy, gateway, endpoint, or network layer and evaluate every interaction as it happens. They apply both content and behavioral checks, block dangerous actions, prevent data loss, and enforce policies across internal apps, customer-facing agents in production, and employee use of AI.

With agents increasingly touching sensitive data and triggering real actions, runtime becomes the safety net for everything governance and identity might have missed.

Without strong runtime guardrails, enterprises expose themselves to unsafe outputs, data leakage, harmful tool execution, and agent behaviors that can escalate into customer impact or operational incidents in seconds.

4. MCP Discovery & Proxy (MCP-Aware Visibility + Enforcement)

MCP adds a completely new layer into the stack: the place where agents actually gain their abilities. It’s neither governance nor identity nor runtime. Governance can tell you what agents exist, and identity can tell you what access they’re supposed to have, but only an MCP-aware layer can see how those agents are wired to tools in real time.

As teams spin up homegrown MCP servers and expose new tools, most enterprises lose visibility into how these components connect and what each agent can actually reach.

This layer provides MCP-aware discovery, indexing every server, tool, and resource across the organization, and sits as a proxy to monitor and enforce policies at the MCP boundary.

Without MCP visibility and proxy enforcement, enterprises end up with uncontrolled tool exposure, shadow MCP servers, and unknown integration paths that allow agents to reach systems far outside their intended scope.

5. Agentic AI Red Teaming (Continuous Attack Simulation)

Agentic red teaming tests what can go wrong when agents operate autonomously. It goes beyond traditional prompt testing and simulates real adversarial scenarios: misbehavior, objective drift, tool abuse, memory manipulation, and malicious chaining of actions across tools and Agents.

Because agents react instantly and operate in unpredictable contexts, one-time testing isn’t enough. Only continuous simulation shows how agents break under pressure. Continuous red teaming surfaces failure modes that governance, identity, and runtime controls may miss, validating the entire action surface from end to end.

Without red teaming, enterprises don’t see dangerous behaviors until they happen in production which is when the damage is already done.

How These 5 Layers Fit Together?

A simple way to think about these 5 layers:

• Governance shows what agents and MCPs exist.

• Identity defines what access they should have.

• Runtime checks what they’re doing right now.

• MCP Proxy controls how they reach tools and resources.

• Red teaming validates how the system fails under pressure

Together, these five layers form the emerging Agentic AI Security "Platform".

Horizontally, the market is also spreading across a 2×2 of what you secure vs. where you enforce, because enterprises are now protecting an entire action surface.

• X-axis (What you secure): Most teams begin by securing prompts and outputs, but the real risk shifts to the agents themselves, the tools they can invoke, the MCP servers that expose those tools, and ultimately the downstream apps, data, and APIs those actions reach.

• Y-axis (Where you enforce): Controls can be enforced at the application layer, at the MCP or gateway boundary, or deeper in the network, endpoint, or identity plane, depending on which team owns AI inside the organization.

This is why the space feels fragmented.

Different teams deploy guardrails in different places, vendors describe similar capabilities from entirely different angles, and the same agent might be secured at three different enforcement points depending on which group touched it first.

In practice, most “agentic” vendors today cluster at the MCP/gateway layer, the identity + runtime layer, or the endpoint/browser layer, while cloud and incumbent security platforms are racing to stretch across multiple layers at once to become the default landing spot for enterprise AI security.

Where This Market Is Headed?

Here is our view of where the market is headed particularly in 2026.

Agentic AI Visibility will unlock the first real budgets. CISOs and CIOs will spend first on the visibility gap, a platform that gives a clear inventory of agents, MCPs, tools, actions, and data flows. Until visibility is solved, nothing else in the stack can operate at scale.

Incumbents will enter from every direction. Cloud, identity, endpoint, AppSec, and networking vendors have and will continue to all bolt on “agentic AI Security” features or modules. The overlap will be noisy and uneven for years, with buyers struggling to map who actually solves what.

Vendors will mature quickly under real buyer needs and budgets. As enterprises deploy agents into production workflows, problem definitions sharpen, vendors get better, faster feedback loops to mature their platforms.

Buyers will run a dual-evaluation strategy. Enterprises will simultaneously evaluate solutions from existing security vendors and also test best-of-breed agentic AI Security platforms. Whoever delivers the clearest value to business becomes their chosen platform.

What about embedding security in the MCP and AppSec infrastructure itself?

We will see and are already seeing signs of security starting to move directly into the infrastructure where agents run inside the agents themselves, inside MCP servers, and inside the AppSec platforms that power downstream applications. Instead of relying solely on external guardrails, enterprises are pushing controls into the core execution layers: agents that validate their own actions, MCP servers that enforce tool boundaries natively, and AppSec systems that understand and monitor AI-driven traffic. As these components evolve, security becomes part of the runtime architecture rather than an add-on.

Final Thoughts

Agentic AI Security is an early but clearly a fast growing, hyper competitive market. 2026 will reward vendors who show real maturity, business value, and strong integration paths as enterprises push Agentic AI deeper into their core business.

More Readings:

• The Current State of AI Agents Adoption in Enterprises

• CISO Strategy for Agentic AI Security in 2026

Terms I’ve not used: I’ve avoided using umbrella terms like AISPM, AI Firewall, AI Safety, and AI TRiSM and similar catch-all labels because they don’t map cleanly to how enterprises are actually deploying agents, MCP servers, tools, and downstream actions. These terms blend very different problems together governance, identity, runtime, MCP wiring, and testing - into a single acronym, which creates more confusion than clarity. The reality inside large organizations is far more layered. Different teams own different parts of the stack, and each gap needs its own control plane. A simple 5-layer model is more accurate and far more useful for anyone trying to understand where vendors fit and what problems they actually solve.