M2M vs. User SSO: Why SSO Stops Working When AI Starts Doing the Work

Summary

Single Sign-On (SSO) transformed how employees access enterprise applications by eliminating password fatigue and centralizing authentication. But AI assistants and autonomous agents introduce a new challenge. When an AI agent needs to connect to a system outside your SSO ecosystem, it often asks for credentials, and many users simply paste them into the conversation.

This isn’t a failure of SSO. It’s the same secrets management problem security teams solved for developers years ago, now spreading to every employee using AI. The first step is eliminating credentials from AI workflows by giving AI agents secretless access to enterprise systems instead of embedded credentials. As AI agents become more autonomous, organizations can then add runtime governance to control what agents are allowed to do.

Isn’t SSO Supposed to Solve Authentication?

Yes, but only for people.

SSO authenticates a human user to applications that participate in the organization’s Identity Provider (IdP). Once authenticated, users can seamlessly access Microsoft 365, Salesforce, Workday, or other integrated enterprise applications without repeatedly entering credentials.

The moment an AI assistant needs to interact with something outside that SSO ecosystem, however, the experience changes.

Maybe it’s:

• A legacy database
• An internal REST API
• GitHub
• Kubernetes
• A cloud provider
• An SSH session
• An external SaaS application

This gap is increasingly recognized by security teams. According to 1Password’s 2025 Access-Trust Gap report, 74% of security and IT professionals believe SSO alone is not sufficient, while many enterprise applications still operate outside centralized SSO. AI agents increasingly need to access exactly these non-SSO systems.

Instead of redirecting the user through SSO, the AI often asks: “Please enter your API key.”

Or “Please provide your username and password.”

At that moment, authentication has moved outside your identity platform, and directly into the LLM conversation.

Why Is Pasting Credentials into an AI Prompt a Security Problem?

Because the credential becomes part of the interaction with the AI model.

Many AI services process prompts externally, retain conversation history based on configuration, or integrate with third-party model providers. Even when enterprise AI platforms implement strong privacy controls, organizations generally want to avoid exposing long-lived credentials to any AI model in the first place.

The user may think they’re simply answering a question.

Security teams recognize they’re exposing a secret.

The trend is already visible in industry research. GitGuardian’s 2026 State of Secrets Sprawl found an 81% year-over-year increase in leaked AI service credentials, while AI-assisted development continues to accelerate the growth of exposed secrets. The problem is no longer limited to developers or source code, it increasingly extends to AI tools, collaboration platforms, and employee workflows.

As AI adoption expands beyond engineering into marketing, HR, finance, legal, and operations, this challenge is rapidly becoming an organization-wide issue rather than a developer-only problem.

Isn’t This Just the Old “Hardcoded Secrets” Problem All Over Again?

It is remarkably similar.

For years, security teams taught developers never to:

• Hardcode passwords
• Commit API keys to GitHub
• Store secrets in configuration files

Instead, organizations adopted secrets management, dynamic credentials, and identity-based authentication.

Now the interface has changed.

Instead of writing:

password = "SuperSecret123"

employees type: “Here’s my password. Please log into the system.”

The medium has changed.

The security problem hasn’t.

Developers accidentally exposed secrets through source code.

Business users now risk exposing them through AI prompts.

Can’t Users Simply Avoid Sharing Credentials?

In theory, yes.

In practice, users optimize for getting work done.

If an AI assistant cannot complete a task without credentials, many users will simply provide them because it’s the fastest way to finish the job.

Security history consistently shows that relying solely on user behavior doesn’t scale.

Organizations didn’t solve developer secrets by repeatedly reminding developers to “be careful.”

They removed the need to handle secrets directly.

The same principle applies to AI.

Is This Really a Machine-to-Machine (M2M) Identity Problem?

Yes.

The moment a user asks an AI assistant to perform work on their behalf, the interaction becomes machine-to-machine.

The AI, not the human, is connecting to APIs, databases, cloud services, SaaS applications, and infrastructure.

Traditional IAM validates the human identity.

The remaining challenge is securely managing the machine acting on the human’s behalf.

As AI agents become more autonomous, identity must extend beyond authentication. Organizations increasingly need to govern not only who or what can access a system, but also what actions can be performed once access is granted. Authentication alone is no longer enough.

Why Doesn’t Traditional IAM Solve This?

Traditional IAM answers one question: Who is requesting access?

AI introduces another: How should the machine access the system?

Even if a user successfully authenticates with SSO, the AI still needs credentials to connect to the target system.

Historically, those credentials have often been:

• API keys
• OAuth tokens
• Passwords
• Service accounts
• SSH keys

Those are exactly the secrets organizations have spent years trying to eliminate.

The challenge isn’t authenticating the human.

It’s authenticating the machine without exposing credentials.

What’s the First Step Toward Securing AI Access?

The first, and often easiest, step is eliminating credentials from the conversation entirely.

Instead of asking users to paste passwords, API keys, or tokens into prompts, organizations can allow AI assistants to access enterprise systems through Akeyless SecretlessAI™.

With Akeyless SecretlessAI™:

• Users never share credentials with the AI.
• AI agents never own or permanently store credentials.
• The Akeyless Gateway brokers access to enterprise systems on the agent’s behalf.
• Authentication is based on identity and policy rather than embedded secrets.
• When credentials are required by a target system, they are issued only for the approved task and automatically expire.
• Secrets stay out of prompts, code, logs, and pipelines.

The AI performs the requested task without ever becoming a privileged identity.

Example: Connecting an AI assistant to SaaS applications

Consider a marketing employee who asks an AI assistant: “Create a dashboard using our HubSpot and Google Drive data.”

Without Akeyless, the AI typically needs API keys or OAuth tokens to access those services. Those credentials often end up in prompts, MCP configurations, local files, or environment variables, creating long-lived secrets that can be exposed or reused.

With Akeyless SecretlessAI™, the employee never shares credentials with the AI. Instead, the AI requests access through the Akeyless Gateway, which authenticates the user, applies policy, and brokers only the approved action. The AI completes the task without ever receiving or storing the underlying credentials.

For organizations already using Akeyless, this extends the same secretless model developers already use into AI workflows. Rather than embedding static credentials, AI agents receive short-lived identities issued only when needed.

Does Removing Credentials Solve the Entire Problem?

Not quite.

Removing credentials dramatically reduces the risk of accidental exposure.

But AI is evolving.

Many organizations are moving beyond assistants that simply retrieve information toward autonomous agents capable of executing production tasks.

At that point, a new question appears: Should the agent actually be allowed to perform this action?

That’s a different problem.

And it requires a different layer of security.

When Do You Need Runtime Authority?

Akeyless SecretlessAI™ eliminates standing credentials by brokering secure access to enterprise systems without exposing secrets to the AI.

Runtime Authority governs what the AI is allowed to do every time it requests access, continuously evaluating intent, policy, and runtime context before allowing an action.

Instead of relying only on static roles or permissions, Runtime Authority evaluates each request before access is granted.

It can:

• Evaluate semantic intent before execution.
• Grant just-in-time, task-scoped access.
• Prevent agents from holding standing credentials.
• Inspect live sessions.
• Mask sensitive responses before they reach the AI.
• Immediately terminate suspicious sessions.
• Create a complete forensic chain from prompt → intent → policy → action.

For example:

An AI agent receives the prompt: “Analyze quarterly sales.”

If the agent attempts:

DROP TABLE customers;

traditional RBAC may still allow the database connection.

Runtime Authority evaluates whether that action matches the user’s intent before the database is ever touched.

How Do Akeyless SecretlessAI® and Runtime Authority Complement Each Other?

Think of them as two stages of AI identity maturity.

If your AI needs to…	Akeyless SecretlessAI®	Runtime Authority
Connect without passwords	✅
Eliminate API keys from prompts	✅
Use short-lived credentials	✅
Execute production actions		✅
Validate user intent		✅
Enforce runtime policy		✅
Mask sensitive responses		✅
Stop unsafe actions mid-session		✅

Organizations don’t have to solve every AI security challenge on day one.

Many can significantly improve security simply by eliminating shared credentials.

Runtime governance becomes increasingly valuable as AI agents begin taking autonomous actions across enterprise systems.

Isn’t This Only Relevant for Developers?

Not anymore.

Developers were simply the first group to encounter the problem.

Today, every department is experimenting with AI:

• Marketing generating reports
• Finance querying ERP systems
• HR retrieving employee information
• Customer support updating tickets
• Sales interacting with CRM platforms

Whenever those AI assistants need credentials outside the SSO ecosystem, users face the same temptation:

Paste the password.

Paste the API key.

Paste the token.

That’s why AI identity security is becoming an enterprise-wide concern rather than a developer-only issue.

We’re Already Using Akeyless. Do We Need to Deploy Anything New?

Very little. If you’re already using Akeyless for developers, most of the foundation is already in place.

Your developers already use Akeyless to eliminate standing credentials from applications and infrastructure. Extending that model to AI is a natural next step. Instead of giving AI assistants passwords or API keys, they access enterprise systems through the same Akeyless platform your organization already operates.

In many environments, there is no need to deploy a separate AI security platform. Runtime Authority builds on the same Akeyless Gateway, authentication methods, identity integrations, and audit pipeline you already use. Enabling AI workflows becomes primarily a matter of connecting the target systems, defining agent policies, and routing AI access through the existing Akeyless infrastructure.

Why Is This Becoming Urgent Now?

AI is rapidly evolving from copilots that generate content into autonomous systems capable of executing business operations.

The 2026 State of AI Agent Identity Security report found that AI agents are already deeply embedded in enterprise environments, frequently accessing sensitive systems, while many organizations report concerns around credential exposure, excessive permissions, and fragmented identity governance.

The challenge is no longer preparing for future AI adoption.

It’s ensuring today’s AI workflows don’t become tomorrow’s security incidents.

Secure AI in Two Steps

Every organization adopting AI faces the same question:

Will users continue pasting credentials into AI conversations, or will AI receive secure, temporary access instead?

With Akeyless SecretlessAI®, organizations eliminate static credentials from AI workflows using just-in-time, short-lived identities.

As AI agents become increasingly autonomous, Runtime Authority builds on that foundation with intent-aware authorization, runtime governance, response masking, and complete forensic traceability.

Whether you’re securing today’s AI assistants or preparing for tomorrow’s autonomous agents, Akeyless provides a practical path from secretless access to full runtime control.

Frequently Asked Questions