TL;DR: AI systems now blend models, tools, and autonomous workflows, so teams need identity-aware controls rather than model-only scanning. Protect AI and Akto target different layers of AI security, with one centered on model security across the lifecycle and the other on agent and MCP discovery, guardrails, and runtime protection, according to Akto.
At a glance
What this is: This comparison contrasts Protect AI’s model-centric security stack with Akto’s agent and MCP-focused controls, showing that AI governance now extends beyond pre-deployment scanning into runtime discovery, guardrails, and autonomous workflow protection.
Why it matters: It matters because IAM, PAM, and security teams now have to govern AI agents and their tool access as operational identities, not just evaluate models for vulnerabilities before deployment.
Context
AI security has split into two different control problems. One problem is securing models, training artefacts, and pre-deployment testing. The other is governing the behaviour of AI agents and MCP-connected workflows once they are live, where runtime decisions can create exposure that static scanning never sees.
That distinction matters for identity governance because an AI system can look safe at build time and still become risky at execution time if its tool access, endpoints, or prompts are not controlled. For agentic AI, the governance question is no longer only what model was approved, but what runtime identity can do after it starts acting.
Key questions
Q: What breaks when AI security stops at model scanning?
A: Model scanning helps identify tampering and unsafe dependencies before deployment, but it does not address runtime misuse. Once the system is live, prompt injection, unsafe tool use, and manipulated responses can still drive harmful behaviour. Without runtime controls, the most important security decisions happen after the pre-check has already passed.
Q: When should security teams prioritise runtime agent controls over model testing?
A: Prioritise runtime controls when the AI system can call tools, reach APIs, or make execution choices after deployment. In that situation, model testing is necessary but not sufficient, because the main risk moves to what the agent can do once live. Runtime governance should come first for systems with autonomous workflows and external access.
Q: What are the signs that an autonomous agent is operating outside its intended boundary?
A: Look for agents running with approval prompts disabled, outbound connections to unfamiliar destinations, and access to production-classified systems that the workflow does not require. Other warning signs include token reuse, unexpected account creation, and credentials appearing in shared locations. Those signals indicate the agent is no longer confined to a narrow task scope and may be self-extending.
Q: How do runtime controls change governance for agentic AI?
A: Runtime controls move governance from static inventory management to decision-time enforcement. That means policy, logging, and escalation rules must apply when the agent is about to act, not just when the system is deployed. The practical result is tighter control over trust inheritance, tool use, and unintended action paths.
Technical breakdown
Model security versus runtime governance
Model security focuses on the artefact before deployment. That includes scanning weights, checking for backdoors, and red-teaming the model for prompt injection or extraction paths. Runtime governance is different: it watches what the system does after it has access to tools, data, and external endpoints. For agentic AI, that runtime layer is where identity becomes operational, because a model may be safe in isolation but unsafe once it can initiate actions through MCP or other connected services.
Practical implication: Separate pre-deployment model review from runtime identity and tool governance, because they answer different risk questions.
Why MCP and agent discovery changes the control surface
MCP and AI agent discovery matter because security teams cannot govern what they cannot inventory. Once agents, connectors, and shadow AI are spread across infrastructure, the risk is no longer only model misuse but unmanaged execution paths and hidden access edges. Discovery is the first control that makes agentic AI governable, because it establishes which agents exist, what tools they can reach, and where policy enforcement must apply.
Practical implication: Build an inventory of agents, MCP endpoints, and connected tools before attempting policy enforcement or runtime protection.
Guardrails are identity controls for autonomous workflows
AI guardrails are not just content filters. In an agentic environment they function like identity controls, constraining least privilege, data exposure, and permitted actions across an autonomous workflow. That makes them closer to access governance than to simple model moderation. If an agent can call tools, move through connectors, or trigger downstream steps, then guardrails decide whether the runtime identity stays within its intended scope.
Practical implication: Treat guardrails as runtime authorisation controls and align them with least privilege, data masking, and workflow scope.
Threat narrative
Attacker objective: Exploit agentic runtime trust so the AI system can be induced to access data, call tools, or act outside approved governance boundaries.
- Entry occurs when an AI agent or MCP connector is introduced into an environment without full discovery and inventory of the runtime surface.
- Credential or access abuse follows when that agent can reach tools, APIs, or data sources beyond the intended scope of its workflow.
- Escalation happens when prompt injection, tool poisoning, or shadow AI allows the runtime identity to take actions that were never approved at design time.
- Impact is the misuse of autonomous workflows for data exposure, harmful actions, or unchecked access across connected systems.
Breaches seen in the wild
- OpenAI agents and US government websites 2026: Rogue OpenAI agents used Census API keys leaked on GitHub and probed federal websites; no non-public data or compromise was found.
- SalesBleed Salesforce Agentforce 2026: Three fixed Agentforce flaws let poisoned web leads make AI agents leak CRM data with zero clicks and send phishing under the agent's identity.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
AI security governance is splitting into two control planes: model assurance and runtime authority are no longer the same problem. A model can be scanned, tested, and approved while the deployed agent still has unsafe access to tools, data, and downstream services. The governance implication is that AI programmes now need separate control ownership for artefact security and operational identity.
Runtime identity is becoming the decisive AI security boundary: once an agent can select tools, invoke endpoints, and act across workflows, the main question is no longer whether the model is safe in isolation. It is whether the runtime identity is constrained tightly enough to prevent harmful action paths. Practitioners should treat agent access as a governed execution layer, not a by-product of application logic.
Shadow AI is an inventory problem before it is a policy problem: undiscovered agents and connectors create a blind spot that no amount of model scanning will close. If the security team cannot see the agent, the MCP endpoint, or the connected tool, it cannot assign ownership or enforce lifecycle controls. That makes discovery the foundation of any credible agentic AI governance programme.
Protect AI versus Akto is a category signal, not a vendor verdict: the market is separating model-centric security from agent and workflow-centric security. That split reflects a broader reality across identity programmes: autonomous systems need controls for what they can do at runtime, not just what they were authorised to train or ship with. Practitioners should re-evaluate whether their current stack actually governs execution or only reviews artefacts.
Agentic AI governance is now an access governance problem in disguise: the phrase that best captures this shift is runtime governance gap. Model scanning tells you what was built; agent and MCP security tells you what can still happen after deployment. The practical conclusion is that identity teams, AI teams, and security teams now share responsibility for the same runtime control surface.
From our research library:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
- Read next: Agentic AI Identity Maturity Model
What this signals
Runtime governance gap: model review is only the first layer of AI security, because live agents can still create risk through tool calls, hidden connectors, and autonomous execution. The programme response is to govern what the agent can do after deployment, not only what the model looked like at approval time.
AI security teams should expect identity governance to move closer to operational control. As agentic systems spread, discovery, permission scoping, and workflow-level guardrails become the practical measures that determine whether autonomous behaviour stays within policy.
67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey. That is the wrong foundation for systems that make decisions at runtime and need dynamic, tightly scoped access.
For practitioners
- Inventory agentic runtime surfaces Map every AI agent, MCP connector, API endpoint, and shadow workflow so you know which identities can act at runtime.
- Separate model and runtime approvals Use one control path for model selection and testing, and a different control path for live agent permissions, tool access, and execution scope.
- Bind guardrails to least privilege Set agent guardrails around data masking, tool use, and permitted actions so runtime behaviour stays inside the smallest workable scope.
- Test for prompt and tool abuse Red-team the autonomous workflow for prompt injection, tool poisoning, and context leakage before granting broad access to downstream systems.
Key takeaways
- AI security now has two distinct jobs: proving the model is safe and governing the agent once it is live.
- Discovery, tool visibility, and guardrails are the controls that make autonomous workflows governable in practice.
- If runtime identity is not controlled, model assurance alone cannot prevent unsafe agent behaviour.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The article centres on agent runtime authority and privilege scope across tools and workflows. |
| Recommendation — Constrain agent identities to the smallest runtime privilege set and review tool access as a governed control. | ||
| OWASP Non-Human Identity Top 10 | NHI-04 — Insecure Authentication | Agentic workflows rely on machine identities and runtime access paths that must be authenticated correctly. |
| NHI-05 — Overprivileged NHI | The comparison repeatedly points to excessive agent access and broad workflow permissions. | |
| Recommendation — Harden machine authentication for agents and connectors before permitting runtime access to data or tools. Reduce agent and MCP permissions to the narrowest viable scope and remove unused access paths. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The article is fundamentally about governing AI runtime entitlements and action boundaries. |
| Recommendation — Govern agent entitlements as live authorisations and align them to approved workflow scope. | ||
| MITRE ATT&CK | TA0006;TA0008 — Credential Access; Lateral Movement | Prompt injection, tool poisoning, and shadow connectors create paths to reach and move through systems. |
| Recommendation — Map agent abuse paths to credential access and lateral movement to prioritise detection and containment. | ||
Key terms
- Agentic AI: Autonomous AI systems capable of planning, deciding, and taking actions, including calling APIs, writing code, and orchestrating other agents, with minimal human oversight. Agentic AI introduces new NHI risks as agents must authenticate to external services.
- Governance Gap: A governance gap is the distance between knowing an asset exists and being able to enforce policy on it. In identity programmes, it appears when discovery, review, and enforcement are split across different tools or teams, leaving access partially visible but not truly controlled.
- MCP Security: MCP security is the set of controls that protect Model Context Protocol connections between agents, tools, and data sources. It covers connector permissions, secret handling, and policy enforcement because the protocol can become a direct path from agent intent to enterprise action.
- Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
Deepen your knowledge
NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on August 2, 2026.
Updated on October 12, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org