Topic starter
09/06/2026 11:14 pm
TL;DR: AI security now has to start in the repository because teams often cannot see where AI is used, what agents are active, or which permissions and MCP connections they hold, according to Lasso Security. That matters because static AppSec controls miss AI-native risks such as prompt injection sinks, unsafe tool execution, and shadow AI before deployment.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Shift AI Security Left: Introducing Lasso’s GitHub Integration”.
Key questions
Q: Where does AI security fail when teams cannot inventory agents and tools in code?
A: It fails at the point where ownership, access scope, and trust relationships become invisible.
Q: Why do AI agents create a bigger governance problem than ordinary endpoint tools?
A: Because an AI agent can execute many file reads, API calls, and transfers in one session without a human approving each step.
Q: What are the signs that an AI project has become shadow AI?
A: The main signs are orphaned agents, unmanaged MCP connections, and AI logic appearing in branches or services without a clear owner or approved governance path.
Practitioner guidance
- Map AI assets in GitHub Connect repository scanning to every codebase that may contain models, agents, MCP servers, or agentic workflows.
- Build a graph of AI trust relationships Track how models, tools, data sources, and connected services interact so you can see which paths create sensitive-data exposure or excessive autonomy.
- Rework prioritisation around blast radius Rank findings by the reach of the affected agent or integration, not by static severity alone, because a small code issue can create broad downstream access.
Bottom line: AI security is moving into the repository because teams need to see models, agents, tools, and MCP connections before deployment.
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This topic was modified 2 days ago by NHI Mgmt Group
10/06/2026 1:30 am
Repository-first AI governance is becoming the new control plane. Security teams cannot rely on production monitoring alone when AI components are assembled in code before they are deployed. The governance problem starts in the repository because that is where hidden agents, tools, and model links first become durable. Practitioners should treat source control as the earliest enforceable identity and access boundary for AI systems.
A few things that frame the scale:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, according to The 2024 ESG Report: Managing Non-Human Identities.
- Enterprises that have experienced a compromised NHI averaged 2.7 separate incidents in the past 12 months, which shows that identity exposure often becomes recurring rather than isolated.
A question worth separating out:
Q: How do organisations reduce prompt injection risk in AI workflows?
A: They should map every point where untrusted content can reach a model or agent that has tool permissions. If the model can translate external text into internal action, the boundary is already too porous. The control objective is to prevent untrusted inputs from becoming executable intent.
👉 Read our full editorial: GitHub-based AI security visibility is the new shift-left control
11/06/2026 12:35 am
Repository-first AI governance is becoming the new control plane. Security teams cannot rely on production monitoring alone when AI components are assembled in code before they are deployed. The governance problem starts in the repository because that is where hidden agents, tools, and model links first become durable. Practitioners should treat source control as the earliest enforceable identity and access boundary for AI systems.
A few things that frame the scale:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, according to The 2024 ESG Report: Managing Non-Human Identities.
- Enterprises that have experienced a compromised NHI averaged 2.7 separate incidents in the past 12 months, which shows that identity exposure often becomes recurring rather than isolated.
A question worth separating out:
Q: How do organisations reduce prompt injection risk in AI workflows?
A: They should map every point where untrusted content can reach a model or agent that has tool permissions. If the model can translate external text into internal action, the boundary is already too porous. The control objective is to prevent untrusted inputs from becoming executable intent.
👉 Read our full editorial: GitHub-based AI security visibility is the new shift-left control
12/06/2026 2:09 am
Repository discovery is becoming the new control plane for AI identity governance: If teams cannot see AI primitives in code, they cannot govern the identities those primitives will later exercise. The article shows that discovery, ownership, and trust mapping are moving upstream into development workflows. The practitioner conclusion is that AI inventory has to begin where AI is assembled, not where it is already running.
A few things that frame the scale:
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to the State of Secrets in AppSec.
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: How do security teams decide which AI agents to fix first?
A: Prioritise the agents that combine business criticality with the broadest dependency chains and the most sensitive data paths. That combination tells you where an unreviewed agent could create the largest blast radius, and it gives development teams a concrete order for remediation.
👉 Read our full editorial: GitHub-based AI security visibility is the new shift-left control
This post was modified 2 days ago by NHI Mgmt Group