Aidome AI Security Essentials

Enterprise AI security is the discipline of governing how AI systems, models, agents, tools and data are used inside an organization, and protecting the people, data and systems they touch.

Traditional security assumes a simple flow: a user connects to an application, which connects to data. Enterprise AI changes that flow. A user now interacts with an AI application, which calls a model, which may retrieve data, call tools, invoke agents and reach external systems.

Every new connection in that chain creates a new control surface. AI security is about making those surfaces visible, governed and defensible.

Shadow AI is the use of AI applications, services, browser tools, local models and embedded copilots that the security team cannot see or govern.

Employees adopt AI quickly because it is useful. That adoption often happens outside approved channels: browser-based chatbots, AI inside SaaS products, developer tools, local model runtimes and direct API calls.

AI security begins with visibility. You cannot secure AI systems you cannot see. The first step is discovery: building an inventory of every AI application, model and service operating across the enterprise.

Prompt injection is when an attacker manipulates an AI system's input to override its instructions, change its behavior or extract sensitive information.

Indirect prompt injection is especially dangerous: a malicious instruction hidden inside a document, email or web page that the AI reads and follows without the user realizing it.

Traditional input validation does not catch prompt injection because the malicious content is natural language, not code. Defenses include input inspection, output filtering, tool authorization and limiting what the AI is allowed to do.

AI systems can expose enterprise data in new ways. Users may paste confidential information into external models, or an AI agent may retrieve and combine data a user should not be able to aggregate.

A user may be authorized to access two pieces of information separately, but should not necessarily be able to ask AI to aggregate, infer or redistribute them.

Data loss prevention for AI means classifying what reaches models, detecting sensitive content in prompts and responses, and enforcing policy on what may be sent to external versus private models.

An AI agent is an AI system that can take actions, not just answer questions. Agents call tools, access systems and execute workflows, sometimes autonomously.

The risk of AI increases dramatically when it moves from generating information to taking action. An agent with email, calendar, CRM, file access and payment APIs is effectively a new privileged digital employee.

Agent security means controlling agent identity, permissions, tool access, data access and the boundaries of what an agent is allowed to do, often with human approval for sensitive actions.

MCP is a protocol that lets AI models and agents connect to external tools, data sources and services. It is how an agent reaches a database, a file store or an external API.

Every MCP connection is a new trust boundary. A malicious or misconfigured MCP server can expose data, execute unwanted actions or become a supply-chain risk.

MCP governance means approving which MCP servers are allowed, controlling which tools they expose, authenticating access, setting permissions and logging every interaction.

AI governance translates organizational AI policy into enforceable controls: which models are approved, which data may reach them, who can use which agent, and what actions require human approval.

Policy is not a document, it is a set of rules the platform enforces. For example: public information may go to an external model, internal information stays on an enterprise model, confidential information uses a private model, and restricted information stays in an isolated environment.

Governance turns acceptable-use intent into routing, identity, DLP and logging that actually run in production.

An AI Control Plane is the layer that sits between enterprise AI applications and the models, agents, tools and data they use. It is where discovery, identity, policy, security, routing, DLP, agent control, observability and audit are enforced as one architecture.

Without a control plane, every AI application enforces its own rules, or none at all. With one, the organization governs AI consistently across every workload.

Aidome is an enterprise AI Control Plane. This course introduces the concept; the ACASF certification builds the full picture.