In short
Injected AI is agentic AI that arrives inside your environment through software you already trust. A SaaS vendor ships an AI assistant, copilot or autonomous agent in a routine release, enables it by default, and lets it inherit the data access your tenant already granted. Nothing was procured, reviewed or approved — yet a new autonomous actor is now reading your data and taking actions on your behalf.
Definition
Injected AI is not prompt injection. Prompt injection is an attack on a model you deployed. Injected AI is a governance and supply-chain event: a vendor injects agentic capability into your estate, behind your back, through the update channel of a product you already licensed and trust.
The mechanics are mundane, which is exactly why it works. A feature flag flips on in a release. A ticketing tool gains an agent that reads every ticket and attachment. A file-sharing suite adds a copilot indexing every document in the tenant. A CRM adds an assistant that can email customers. None of it required a new contract, a new login, or a new network connection — the AI runs with permissions your organization granted the vendor years earlier, for a different purpose.
The result is an autonomous actor with production data access that never appeared in a security review, a DPIA, a vendor questionnaire or an AI inventory. It is invisible to the controls built for people and for software, because it is neither: it is a third party's agent operating inside your trust boundary, under your vendor's roadmap rather than your policy.
Security risks
Default-on activation
AI features ship enabled and opt-out. The decision to run an agent over your data is made in the vendor's release notes, not in your change process.
Silent permission inheritance
The new agent reuses the OAuth scopes, service accounts and tenant-wide access already granted to the product — instantly maximal, never re-scoped for AI use.
Undisclosed data flow
Your regulated content can be sent to a model provider, a new sub-processor or a new region without a contract update, breaking data-residency and DPA commitments.
Training and retention drift
Prompts, documents and outputs may be retained or used to improve vendor models under terms that changed in an update to the acceptable-use policy.
Autonomous action inside your tenant
Assistants that only summarised last quarter can now send mail, modify records, move files or call other tools — the blast radius grows with each release.
Fourth-party agent chains
Your vendor's agent calls another vendor's model or MCP server. Your data crosses two trust boundaries you never assessed, and one incident becomes three.
Audit and attribution gaps
Vendor-side agent actions appear in logs as the integration's service identity, so you cannot prove who — or what — took an action during an investigation.
Compliance exposure
EU AI Act, GDPR, HIPAA, SOC 2 and DORA all assume you know which automated systems process your data. Injected AI breaks that assumption silently.
Examples
Collaboration suite copilot enabled tenant-wide
A productivity vendor turns on an assistant that indexes every document, chat and mailbox in the tenant. Over-shared HR and legal files become instantly retrievable by any employee who asks the right question.
Support desk agent reading attachments
A ticketing platform adds auto-triage. Customer PII and screenshots inside tickets are now processed by a model provider that never appeared on the sub-processor list.
CRM assistant with send authority
A sales platform upgrades its summariser into an agent that can draft and send customer emails, giving a vendor-controlled system outbound communication in your brand's name.
Code assistant in the developer toolchain
A repository host enables AI review across private repositories. Proprietary source and secrets in history are read by a third-party inference service.
Browser and endpoint extensions
An approved extension adds an agentic sidebar that reads whatever the employee has on screen — including internal admin consoles and customer records.
Marketplace app inherits AI
A small marketplace add-on adds an AI feature powered by a fourth-party API, extending your data flow to a company you have never evaluated.
Architecture
Vendor release channel
The injection path: feature flags, auto-updates and marketplace apps that change capability without changing the contract.
Inherited authorization surface
OAuth grants, service accounts, tenant admin consent and API tokens the AI feature silently reuses.
Continuous AI feature discovery
Detection of newly appearing AI capability across SaaS tenants, browser extensions, marketplace apps and integration identities.
Data-flow observation
Egress and API telemetry that reveals when a trusted product starts sending your content to a model endpoint or a new region.
Policy and consent gate
A default-deny posture for new autonomous capability, with named owners approving activation before it reaches production data.
Evidence and audit trail
A durable record of which AI features exist, who approved them, what they can access, and what they actually did.
Controls
Inventory AI capability, not just applications
Track the AI features inside each approved product as first-class assets, with owner, data scope, sub-processors and activation state.
Default-deny new AI features
Where the platform allows it, disable AI capability tenant-wide and re-enable per use case after review. Treat opt-out defaults as an unapproved change.
Re-scope inherited permissions
Do not let an AI feature run on the access granted to its host product. Require dedicated, least-privilege scopes and separate identities for agentic functions.
Contract for AI change notice
Require advance notification of new AI features, model providers and sub-processors, plus training-exclusion and retention limits, in every renewal.
Monitor vendor release channels
Watch release notes, admin-console toggles and marketplace updates continuously — activation is a change event even when the vendor does not call it one.
Detect the behaviour, not the announcement
Alert on new model endpoints, new integration identities and step-changes in data volume read by a trusted product.
Constrain the blast radius
Deny write, send and delete capability to vendor agents by default, require approval for irreversible actions, and log every one.
Rehearse revocation
Know exactly how to disable an AI feature, revoke its grant and prove deletion — before an incident or a regulator asks.
Comparison
Injected AI is often confused with prompt injection or shadow AI. They differ in who introduces the AI and where the control point sits.
| Concept | Who introduces it | Where it runs | Primary control |
|---|---|---|---|
| Injected AI | Your trusted SaaS vendor, via an update | Inside a product you already approved | Vendor governance, feature-level inventory, permission re-scoping |
| Shadow AI | Your own employees or teams | Unapproved tools outside procurement | Discovery, sanctioned alternatives, acceptable-use policy |
| Prompt injection | An external attacker | Inside a model or agent you deployed | Trust separation, input handling, capability limits |
| Sanctioned AI | Your organization, deliberately | Systems you designed and reviewed | Design-time controls, evaluation, runtime monitoring |
Frequently asked questions
Is injected AI the same as prompt injection?
No. Prompt injection is an attack technique against a model or agent you run. Injected AI describes agentic capability inserted into your environment by a trusted vendor through a normal product update, without a procurement or security decision on your side.
How is injected AI different from shadow AI?
Shadow AI is introduced by your own people using unapproved tools. Injected AI is introduced by your supplier inside an approved tool, which is why it passes every application-level control you have.
Why is inherited permission the core problem?
The AI feature does not ask for new access. It reuses the tenant-wide grants you gave the product years ago, so an assistant introduced for convenience can read everything the integration could ever read.
How do we find injected AI in our estate?
Combine three signals: admin-console and release-note monitoring for each major SaaS, identity telemetry for new or newly active integration principals, and network or API evidence of traffic to model endpoints from products that never used them before.
What should be in the contract?
Advance notice of AI feature activation, a maintained list of model providers and sub-processors, training exclusion for your content, retention and residency limits, audit rights over agent actions, and the right to disable AI features without losing the underlying service.
Can we simply block it?
Rarely wholesale, and blocking is not the goal. The goal is that no autonomous capability reaches production data without a named owner, a scoped permission and a monitored blast radius.
Sources
How Andeavour addresses it
Discovery of vendor-injected agents
AgentGuard inventories agentic capability appearing inside approved SaaS, extensions and marketplace apps — including features enabled by a vendor without notice — with zero integration into the systems being inventoried.
Permission and data-flow visibility
Every discovered agent is mapped to the identity it borrows, the data it can reach and the model endpoints it talks to, so inherited tenant-wide access is visible before it is abused.
Policy enforcement and containment
Default-deny policy for new autonomous capability, approval gates for write and send actions, and one-action revocation when a vendor agent exceeds its mandate.
Audit-ready evidence
A timestamped record of which AI features appeared, when they activated, who approved them and what they accessed — the artefact regulators and customers ask for.
See it against your own environment
A 30-minute technical session with the Andeavour team — no integration required to start.