The Missing Control Layer for AI Agents
AI agents are no longer just generating text - they are operating across systems autonomously. Traditional security tools were never designed for this. Sekuire provides the missing governance and runtime control layer.
The Missing Control Layer for AI Agents
Software once executed deterministic instructions written entirely by humans. Today, AI agents write code, query databases, send emails, approve expenses, update CRM records, provision infrastructure, and take action across dozens of systems on their own.
They are no longer just generating text. They are operating.
This shift is profound. And it introduces a new class of risk that traditional security, IAM, and observability tools were never designed to handle.
AI agents are dynamic, autonomous, tool-using systems. They can reason, chain actions together, and make decisions in real time. Yet most organizations are deploying them with little more than API keys and logging.
That gap is exactly why Sekuire exists.
The New Risk Surface: Autonomous Action
When an AI agent is connected to Slack, Google Workspace, GitHub, Stripe, or internal APIs, it effectively becomes a digital operator. It can:
•Read and send messages
•Access and modify documents
•Execute code
•Trigger workflows
•Call external services
•Chain together multiple tools
The problem is not that agents are powerful. The problem is that their power is often unbounded and invisible.
Traditional IAM systems answer questions like:
•Who has access to this system?
•What role does this user have?
But AI agents are not static users. They are dynamic actors. They change prompts. They switch tools. They operate across contexts. They generate new instructions at runtime.
Logging tools tell you what happened after the fact. They do not prevent it.
What organizations need is runtime control.
Identity Is Not Enough
Most companies treat an AI agent like a service account. They give it an API key and permissions, and that is the end of it.
But AI agents are composable. Change the system prompt, the model, or the available tools, and you have effectively created a new agent. Yet in most environments, there is no cryptographic or structural way to distinguish one configuration from another.
If a prompt is modified or a tool is swapped, how would you know?
If an agent begins acting outside its intended scope, what stops it?
If a model behaves unexpectedly, can you instantly revoke it?
Without strong identity and policy enforcement, you are trusting runtime behavior you cannot fully predict.
From Visibility to Control
The current AI infrastructure ecosystem focuses heavily on observability. Dashboards show traces, tokens used, model latency, and output logs. These are valuable.
But visibility is not control.
You do not secure cloud infrastructure by logging access. You secure it by enforcing policy at runtime.
AI agents require the same shift.
Sekuire is a governance and control layer that sits between AI agents and the systems they interact with. It does not host your agents. It does not replace your models. It does not dictate your architecture.
Instead, it enforces what agents are allowed to do, in real time.
What Sekuire Solves
1. Verifiable Agent Identity
AI agents are defined by their model, system prompt, and tools. Sekuire treats this combination as a cryptographically verifiable identity.
This ensures that:
•An agent cannot silently change its behavior without detection
•Policies are tied to a specific agent configuration
•You know exactly which version of an agent executed a task
In a world where prompts are easily modified and models are frequently swapped, identity must be content aware.
2. Policy as Code for Agents
Security policies for humans are well established. AI agents need the same rigor.
With Sekuire, organizations define machine-enforceable policies that govern:
•Which tools an agent can call
•Which data it can access
•What actions it can perform
•Under what conditions those actions are allowed
These policies are enforced at runtime. If an agent attempts an unauthorized action, it is blocked immediately.
This shifts governance from “review later” to “enforce now.”
3. Real Time Runtime Enforcement
AI agents operate unpredictably by design. They reason. They plan. They adjust.
That makes static permissioning insufficient.
Sekuire evaluates every agent action as it happens. If a task falls outside approved boundaries, it is stopped before damage occurs.
This prevents:
•Accidental data exposure
•Over privileged tool usage
•Prompt injection leading to unauthorized actions
•Escalation across systems
Control happens during execution, not after.
4. Task Delegation Tracing
AI agents often delegate subtasks to other agents or services. Without tracing, it becomes impossible to understand how a final action was reached.
Sekuire provides structured tracing of task delegation chains. You can see:
•Which agent initiated a task
•What intermediate steps were taken
•What tools were invoked
•What final action was executed
This is essential for auditability, debugging, and compliance.
5. Immutable Audit Logs
Enterprises require defensible audit trails.
Sekuire maintains immutable logs of agent actions, tied to verified agent identities and policy decisions. This enables organizations to meet internal governance requirements and external compliance obligations.
When regulators or security teams ask, “Why did this happen?” there is a verifiable answer.
6. Instant Kill Switch
AI systems can fail in unpredictable ways. Models can hallucinate. Prompts can be compromised. Integrations can break.
When something goes wrong, response time matters.
Sekuire provides immediate revocation of agent credentials and runtime permissions. Organizations can disable an agent instantly, preventing further action across connected systems.
This is the difference between an incident and a contained event.
Governance for the Agent Era
Every major shift in computing required a new control plane.
Cloud computing introduced infrastructure as code and cloud IAM. SaaS proliferation led to identity providers and access governance. APIs led to API gateways and rate limiting.
AI agents introduce a new operational layer: autonomous digital operators.
They require their own governance model.
Sekuire is designed to complement, not replace, existing security systems. IAM handles identity for people. Observability platforms provide visibility. SIEM tools analyze events.
Sekuire focuses specifically on controlling AI agents at runtime.
Why This Matters Now
AI agents are rapidly moving from experimentation to production.
They are handling customer support, internal operations, financial workflows, development pipelines, and infrastructure management.
As their autonomy increases, so does the blast radius of a mistake.
Organizations cannot rely solely on trust in model behavior. They need deterministic enforcement around nondeterministic systems.
That is the paradox of AI governance.
You cannot predict every output. But you can control what actions are allowed.
The Future of Secure Autonomy
AI agents will become foundational to modern organizations. They will operate continuously, collaborate with each other, and execute complex workflows without direct human oversight.
The companies that succeed will not be those who deploy the most agents. They will be those who deploy them safely.
Governance must evolve from human centric access control to machine centric runtime enforcement.
Sekuire exists to provide that missing control layer.
If you are building or deploying AI agents and want to enforce what they can do in real time, you can request access at:
Sekuire: The Runtime Control Layer for AI Agents
Modern AI agents don’t just generate text—they operate. They write code, send emails, update CRMs, trigger workflows, and act across your SaaS and internal systems. That makes them powerful, but also introduces a new class of risk that traditional IAM, logging, and observability were never built to handle.
Sekuire is the missing control layer for this new era of autonomous, tool-using AI agents.
The Problem: Autonomous Action Without Control
Once connected to systems like Slack, Google Workspace, GitHub, Stripe, or internal APIs, an AI agent effectively becomes a digital operator that can:
•Read and send messages
•Access and modify documents
•Execute code and scripts
•Trigger workflows and pipelines
•Call external services and APIs
•Chain multiple tools and actions together
The issue isn’t that agents are powerful—it’s that their power is often unbounded and invisible.
Traditional IAM answers:
•Who has access to this system?
•What role does this user have?
But AI agents are dynamic actors:
•Change the system prompt, model, or tools and you’ve effectively created a new agent
•They reason, plan, and generate new instructions at runtime
•They operate across multiple contexts and systems
Logging and observability tell you what happened after the fact. They don’t prevent it.
Organizations need runtime control, not just visibility.
Why Identity Alone Fails for Agents
Most companies treat an AI agent like a service account: give it an API key, assign permissions, and ship it.
This breaks down because agents are composable:
•A specific agent is defined by its model, system prompt, and tooling
•Modifying any of these effectively creates a new behavior profile
Without a way to bind identity to configuration:
•You can’t tell when a prompt or toolset has changed
•You can’t reliably scope policies to a specific agent configuration
•You can’t instantly revoke or constrain a misbehaving variant
You end up trusting runtime behavior you cannot fully predict or verify.
From Observability to Runtime Enforcement
The current AI stack is heavy on observability:
•Traces, token counts, latency metrics
•Output logs and dashboards
These are useful, but visibility is not control.
We don’t secure cloud infrastructure by just logging access—we secure it by enforcing policy at runtime.
AI agents require the same shift.
Sekuire is a governance and control layer that sits between AI agents and the systems they interact with. It:
•Does not host your agents
•Does not replace your models
•Does not dictate your architecture
Instead, it enforces what agents are allowed to do, in real time.
What Sekuire Provides
1. Verifiable Agent Identity
Sekuire treats the combination of model + system prompt + tools as a cryptographically verifiable identity.
This ensures that:
•An agent cannot silently change its behavior without detection
•Policies are bound to a specific, verifiable agent configuration
•You know exactly which version of an agent executed a given task
In a world of easily edited prompts and frequently swapped models, identity must be content-aware, not just key-based.
2. Policy as Code for Agents
Human security policies are mature. AI agents need the same rigor.
With Sekuire, you define machine-enforceable policies that govern:
•Which tools an agent can call
•Which data it can access
•What actions it can perform
•Under what conditions those actions are allowed
These policies are enforced at runtime. If an agent attempts an unauthorized action, it is blocked immediately.
Governance shifts from “review later” to “enforce now.”
3. Real-Time Runtime Enforcement
Agents are intentionally nondeterministic:
•They reason, plan, and adapt
•They can be influenced by prompts, data, and external inputs
Static permissioning is not enough.
Sekuire evaluates every agent action as it happens. If a task falls outside approved boundaries, it is stopped before damage occurs.
This helps prevent:
•Accidental data exposure
•Over-privileged tool usage
•Prompt injection leading to unauthorized actions
•Lateral or privilege escalation across systems
Control happens during execution, not after the incident.
4. Task Delegation Tracing
Agents rarely act alone. They:
•Delegate subtasks to other agents
•Call internal services
•Chain tools and workflows
Without structured tracing, it’s nearly impossible to answer: “How did we get from the initial request to this final action?”
Sekuire provides task delegation tracing, so you can see:
•Which agent initiated a task