What it is
AI governance is how your company decides which AI tools people may use, what data can go into them and how that is enforced. AI security covers the technical side: keeping sensitive data out of the wrong tools, finding AI use you did not approve and controlling what AI agents and models can access.
The products in this space fall into a few groups:
Data loss prevention (DLP) for AI watches what employees paste or upload into AI tools and can warn, block or redact sensitive content. Some products extend existing DLP across endpoints, browsers and software as a service (SaaS) apps.
Data security posture management (DSPM) finds and classifies sensitive data across cloud, SaaS and on-premises storage. That matters for AI because tools connected to your file shares can surface anything their users can technically reach.
Shadow AI discovery identifies which AI tools and browser extensions are actually in use, often from network, browser or identity data.
AI agent and model security covers runtime monitoring of AI workloads, identity and permissions for agents, guardrails for internal AI applications and testing such as AI red teaming.
Policy enforcement ties these together: applying your rules by user, group, tool and data type.
The key point: policy and decisions come before tooling. A product can enforce rules, but it cannot decide what your rules are. Without an acceptable-use policy, an approved tool list and clear data handling rules, you will buy a dashboard full of activity you have no basis to act on.
Signs you need it
- Staff are using AI assistants for work and nobody has said which ones are allowed.
- Someone has pasted customer data, source code or contract text into a public AI tool.
- A vendor turned on AI features in a product you already use, and you are not sure where the data goes.
- You are rolling out an AI assistant connected to email, files or chat, and permissions on those files have never been reviewed.
- Teams are building AI agents or automations that act with real credentials.
- Customers or auditors are asking how you control AI use and protect their data.
What to look for
- Coverage of how people actually use AI. Browser, desktop apps, mobile and built-in AI features in SaaS each need different controls.
- Context, not just blocking. Warning a user and explaining the rule changes behavior without sending people to personal devices.
- Accurate classification. Detection that misreads your data produces either noise or false confidence.
- Granular policy. You will want different rules for different groups, tools and data types.
- Agent identity and permissions. Agents should run with their own scoped credentials and leave an audit trail.
- Privacy of the tool itself. A product that inspects prompts holds sensitive data, so check where it stores and processes it.
- Fit with existing controls. Reuse your identity provider, DLP and endpoint tooling where they already cover part of the problem.
Mistakes to avoid
- Buying before deciding. Write the policy and approved tool list first, then pick tools that enforce it.
- Blanket bans. Blocking every AI tool usually moves use to personal phones and accounts, where you see nothing.
- Ignoring file permissions. An AI assistant connected to your file shares exposes old oversharing faster than any attacker.
- Treating vendor AI features as someone else's problem. Review terms for data retention and model training for the tools you already pay for.
- Giving agents broad standing access. Scope credentials tightly and require human approval for actions that move money, data or access.
Questions to ask vendors
- Which AI tools, browsers and apps do you cover today, and how do you detect new ones?
- Can a policy warn, require justification, redact or block, and can that differ by group?
- Where are captured prompts and files stored, who can see them and how long are they kept?
- How do you classify sensitive data, and can we add our own data types?
- How do you handle AI features built into SaaS products we already use?
- What visibility and control do you provide for AI agents and the credentials they use?
- What does deployment require on endpoints, browsers or the network?
- How do you report on usage and policy violations for management?
What drives the cost
Most products in this space price per user, per endpoint or by the volume of data scanned. DSPM and data discovery tools often scale with the number of data stores and the amount of data classified. Agent and model security tools may price by workload, application or number of agents. Effort is a bigger factor than it looks: writing policy, reviewing file permissions, tuning classifiers, handling user exceptions and training staff take real time. Overlap with tools you already own, such as identity, endpoint or existing DLP, can reduce what you need to buy.
Where Tabor Point can help
Tabor Point provides AI governance services: writing an AI acceptable-use policy, vetting AI tools and vendors and setting data handling rules your staff can follow. That groundwork makes it clear which controls you actually need, if any, before you commit to a product. If you want a second opinion before you buy, Tabor Point can help.
