The speed at which enterprises are deploying AI agents has outpaced the guardrails meant to control them. In September 2026, two cybersecurity vendors -- Orchid Security and Akeyless -- announced products that address a problem most enterprises have not yet realized they have: what happens when an autonomous AI agent encounters the identity debt embedded across your systems?
This is not a theoretical risk. Orchid Security's research, published September 11, 2026, found that AI agents do not need to "break" security controls to escalate privileges. They can find and use the identity debt already embedded across the enterprise: hard-coded credentials, orphaned accounts, unmanaged authentication paths, and excessive permissions. An agent completing an authorized objective can drift beyond its initial privilege level within seconds, not through malicious intent, but through accessible pathways that were never properly locked down.
Akeyless responded on the same day with the general availability of its Agentic Runtime Authority -- a real-time identity control layer that restricts what agents can do once they have access. Together, these announcements signal that the industry is waking up to a governance gap that has been widening since enterprises began deploying agents at scale.
What Happened: Two Products, One Problem
On September 11, 2026, Help Net Security reported on a wave of security product releases targeting AI agent risk:
Orchid Security announced identity drift detection and application-level kill switches for AI agents. Their core finding: agents can complete authorized objectives beyond their initial privilege level within seconds by exploiting identity debt -- credentials, accounts, and permissions that were never cleaned up.
Akeyless released Agentic Runtime Authority, a real-time enforcement layer that applies intent-based access control to agent actions. Rather than just protecting credentials (their existing SecretlessAI product), the new layer governs what agents do after they authenticate.
The timing is not coincidental. A report from Comarch the same week noted that AI agent adoption in enterprise finance has reached 40%, with e-invoicing and accounts payable as primary use cases. More agents in production means more exposure to identity-based risk.
Why It Matters: Identity Debt Is the Real Attack Surface
Identity debt is the accumulation of credentials, permissions, and access paths that organizations never properly managed. It includes:
- Hard-coded API keys left in configuration files
- Service accounts that were never deactivated after a migration
- Overly broad permissions granted to "temporary" integrations
- Authentication paths that bypass MFA because they were set up before MFA existed
When a human employee encounters identity debt, the damage is limited by the human's session duration, attention span, and the fact that someone might notice unusual behavior. When an AI agent encounters the same debt, it can parse, exploit, and act on dozens of credentials in seconds, operating at a speed and scale that traditional monitoring cannot track.
Orchid's kill switch concept is straightforward: if an agent begins operating outside its intended scope -- accessing systems it should not, escalating privileges, or executing actions beyond its mandate -- an application-level kill switch halts it immediately. Think of it as a circuit breaker for autonomous software.
The Three Pillars of Agent Governance
Based on the patterns we see across our client work at Vivid Visuals, effective agent governance rests on three pillars:
| Pillar | Question | Control |
|---|---|---|
| Identity | Is this agent who it claims to be? | Unique credentials per agent, short-lived tokens, no shared service accounts |
| Authorization | What is this agent allowed to do? | Intent-based access control, least-privilege scopes, action allowlists |
| Runtime | What if it goes wrong? | Kill switches, action logging, real-time drift detection |
Most organizations have identity partially covered (single sign-on, MFA for humans). Few have authorization for non-human actors. Almost none have runtime controls. The gap between "we deployed an agent" and "we can stop that agent" is where risk lives.
Who It Affects
If your organization has deployed any AI agent -- whether through Copilot Studio, Agentforce, a custom build, or even a no-code platform like Base44 -- the governance question applies to you:
- SMBs running workflow agents: If your agent has access to your CRM, email, or billing system, it has the keys to sensitive data. Without runtime controls, a misconfigured prompt or unexpected input can trigger actions you did not authorize.
- Mid-market operations teams: Agents managing invoices, reconciliation, or customer communications need scoped credentials. "Full access to QuickBooks" is not an acceptable permission for an agent that only needs to read invoice totals.
- Enterprise IT and security teams: Identity drift detection is no longer optional. The longer an agent operates, the more likely it is to discover and use dormant credentials. Continuous monitoring is the only defense.
Practical Steps: Building Governance Before You Need It
At Vivid Visuals, we implement agent governance as a design constraint, not a post-deployment patch. Here is the framework we apply:
1. Audit Your Identity Debt
Before deploying any agent, inventory every credential, service account, and API key in the systems the agent will touch. Disable what is not needed. Rotate what is stale. This alone eliminates 80% of the risk.
2. Assign Unique Agent Identities
Never share a human user's credentials with an agent. Create a dedicated identity with a scoped role, short-lived tokens, and an expiration date. If the agent is decommissioned, the identity dies with it.
3. Define Action Boundaries
Use intent-based access control: define what the agent is allowed to do, not just what systems it can access. "Read invoice data" is a scope. "Read all financial records" is not.
4. Implement Kill Switches
Every agent should have an application-level kill switch that halts execution immediately. This is not a feature -- it is a requirement. If you cannot stop an agent in under one second, you do not have control.
5. Log Everything
Agent actions should be logged with enough detail to reconstruct what happened, what credentials were used, and what data was accessed. This is your forensic record if something goes wrong.
The Cost of Waiting
The organizations most exposed to agent governance risk are the ones that moved fastest. They deployed agents to impressive effect, generated measurable ROI, and accumulated identity debt along the way. The launch of Orchid's and Akeyless's products signals that the market recognizes this gap -- but tooling alone does not solve it. Governance is a discipline, and it must be designed into your agent architecture from the start.
If your team has deployed AI agents without a governance framework, the time to build one is now -- before an agent drifts into a system it was never meant to touch.
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