AI security solutions: what they are and how to choose one

Employees can start using an AI tool and share business information with it before IT has reviewed the service or the data involved. AI security solutions help businesses bring that exposure under control without treating every useful AI interaction as something to ban.

This guide is written for SMB IT decision-makers evaluating protection for employees who use public or business generative AI tools, often with support from a managed service provider (MSP). The focus is governing employee use, not securing proprietary model development or testing custom AI applications.

Summary: AI security solutions are technologies and services that reduce risks associated with using or developing artificial intelligence. For SMBs using generative AI, the practical priorities are discovering shadow AI, controlling GenAI data leakage and reducing prompt injection risk. Effective protection connects those controls to an AI usage policy, documented coverage and a management process that the business or its MSP can run consistently.

Quick definition: AI security solutions help organizations govern AI use, protect sensitive information and reduce AI-specific threats such as prompt injection.

AI security covers several distinct buying problems. A business building its own AI application may need security testing, access controls for connected systems and safeguards around model inputs and outputs. A business using existing GenAI services needs visibility into which tools employees use, what they submit and which interactions should be restricted.

AI use is already widespread. In McKinsey's 2025 survey, 88% of respondents reported regular AI use in at least one business function at their organizations. That is a survey finding, not an SMB-specific adoption rate (McKinsey, 2025).

AI security solutions are not the same as cybersecurity products that use AI. A threat-detection tool might use machine learning to identify suspicious behavior or GenAI to summarize an investigation; that is AI-powered cybersecurity. Protecting an employee's interaction with an AI service is a different job.

AI security is not one product category with one set of controls. Discovery, data protection, application access control and prompt-abuse detection address different parts of the problem. Buying one capability does not establish coverage for every AI system or workflow.

AI security solutions are also not interchangeable with traditional data loss prevention (DLP). DLP protects sensitive information across specified channels, which may include AI interactions. Discovering unapproved AI use and detecting instructions that manipulate a model, however, are not automatically included in a DLP deployment.

When assessing generative AI security, start with a practical distinction: are you protecting AI that your organization builds or AI that its employees use? The answer should shape the shortlist before you compare features.

Shadow AI, GenAI data leakage and prompt injection are three common risks for organizations using public and business GenAI tools, though they are not an exhaustive list.

They can occur separately or together. An employee may use an unapproved tool, submit sensitive information to an approved one or encounter malicious instructions while working with external content. Each situation needs a different control.

Shadow AI is the use of AI applications or services outside an organization's approved or governed technology processes.

A marketing employee might open a personal AI account to draft copy, while a finance employee might use an unapproved chatbot to analyze a spreadsheet. Neither action has to be malicious to create uncertainty about the service, account ownership or information being shared.

Verizon's 2026 Data Breach Investigations Report announcement identified shadow AI as the third most common non-malicious data-leakage-related activity (Verizon, 2026).

Discovery should help IT separate useful, permitted activity from use that needs restrictions. A personal account is not automatically proof of a policy violation, but it can raise questions about business oversight and account controls. An approved application, in turn, does not make every use of it acceptable.

Inventory the tools, identify their users and document the business purpose. Where activity is already known to be prohibited, block it rather than waiting for the inventory to be complete. For a deeper treatment, see what shadow AI is and how to govern it.

GenAI data leakage occurs when sensitive information is exposed through an AI interaction without appropriate authorization or safeguards.

Exposure can begin when an employee pastes a client record, medical detail, payment information or internal strategy into a prompt. File uploads can widen the problem because they may contain more information than the employee intended to share.

An approved AI service can still receive data that company policy prohibits. Approval therefore needs to cover the service, account arrangements and permitted data, not just the application's name.

Define what must not be submitted, identify the interactions that require inspection and decide which violations should be blocked. Access restrictions and content inspection have different purposes: preventing access to a service is not the same as examining the information sent to it.

For implementation guidance, see how to prevent AI data leakage.

Prompt injection occurs when instructions in an AI input cause a language model to behave or produce output in unintended ways.

The category LLM01:2025 Prompt Injection describes this vulnerability in the OWASP Top 10 for LLM Applications. Direct injection comes through a prompt submitted to the model. Indirect injection arrives through external material, such as a website or document that the model processes (OWASP Top 10 for LLM Applications, 2025).

An employee may ask an assistant to summarize a document without realizing it contains instructions aimed at the model. Depending on the application and its permissions, those instructions could influence the response or try to redirect a connected workflow.

Filtering can reduce exposure, but prompt injection is not completely preventable. OWASP recommends layered mitigations, including limiting privileges and requiring human approval for high-risk actions. For practical examples, see what prompt injection is.

Good SMB AI security turns the way people actually use AI into a manageable set of policies, controls and responsibilities.

Enterprise-first platforms often emphasize model development, application testing and complex cloud infrastructure. Those capabilities may be necessary for a company building AI systems, but they are not automatically the right starting point for a smaller business whose immediate concern is employee use of GenAI services.

For employee GenAI use, AI security best practices should begin with approved tools, clear data rules and an owner responsible for enforcement.

Make the policy usable. Employees should know which services and accounts are permitted, which information must stay out of prompts and where to request an exception. A rule that only says "use AI responsibly" leaves too much room for interpretation when someone is simply trying to finish a task.

Choose deployment modes according to risk. Detect-only operation can help assess lower-risk workflows and tune policies, while known prohibited services or clearly restricted information may justify immediate blocking. Monitoring is useful, but it is not a substitute for enforcement once the business has decided an activity is unacceptable.

Assign someone to review violations and exceptions. That may be an internal IT owner working with the MSP rather than a dedicated security analyst.

Controls need to sit where they can observe or restrict the AI interactions the business intends to govern.

Endpoint-based delivery places protection on managed devices. Browser-focused controls address supported web interactions, while gateway and application-level approaches come with different deployment requirements. None should be assumed to see every AI interaction simply because it can identify the application.

For a mixed Windows and macOS estate, check each required feature on each operating system. Support for a vendor's general endpoint agent does not establish support for every GenAI feature. Browser versions, application support and unmanaged-device use also belong in the coverage assessment.

Test everyday work, not only a scripted demonstration. With synthetic data, try an approved task, a prohibited submission and an unapproved service. Check what the employee sees and whether the event reaches the person responsible for responding.

An MSP-delivered AI security service should make clear who maintains policies, reviews events and reports on the client's exposure.

The term AI security service can describe a one-time assessment, a software subscription or recurring operational management. Ask which work is included, because installing a control is not the same as maintaining the policy behind it.

Agree on an exception process and a reporting schedule. A useful report explains what happened, which controls acted and what still needs a decision, rather than simply presenting a large alert count.

Evaluate an AI security solution against your own applications, data rules and management arrangements, then have the vendor demonstrate the results.

These five questions help distinguish a useful operational control from a feature list when comparing AI security tools for SMBs and MSPs.

A useful solution identifies relevant AI use and provides controls that match the business's policy.

Ask which services it recognizes, where visibility is collected and whether events identify the user as well as the device. Establish whether an application can only be discovered, can be allowed or blocked or supports inspection of its AI interactions. These are separate capabilities.

The business should be able to see the consequences of each mode and apply enforcement according to risk.

Ask the vendor to show a detected violation and a blocked submission using synthetic information. Confirm which controls support each mode, what the user sees and what is recorded. Assess legitimate work that might be incorrectly blocked as well as prohibited activity that the control might miss.

Good integration reduces duplicated operational work; it does more than place a link between two dashboards.

Ask what additional agent, console, identity integration or reporting process is required. Have the vendor demonstrate how an administrator creates a policy, investigates an event and produces a report. Include deployment and ongoing management effort in the buying decision, not just the subscription price.

In a multitenant MSP model, centralized operation should not blur the separation between each client's policies and information.

Ask the provider to demonstrate client-specific configuration and reporting. Confirm who can change policy, review events and authorize exceptions. A business with different data obligations should not inherit another client's rules simply because both use the same service provider.

Require feature-level documentation for every relevant application, interaction type and operating system.

Distinguish typed prompts, pasted content and file uploads from simple application access control. Ask about supported formats and exclusions rather than interpreting "file protection" as universal coverage. Test Windows and macOS requirements separately, including the versions used in your environment.

Use this checklist to record the vendor's answers before buying.

Application approval does not make every interaction permitted or inspected.

An AI security framework connects business policy with responsibilities, risk assessment and evidence that controls are working.

Governance can lag behind adoption. In ISACA's 2026 AI Pulse Poll, 38% of respondents reported that their organizations had a formal, comprehensive AI policy. The poll surveyed more than 3,400 digital trust professionals (ISACA, 2026).

The NIST Artificial Intelligence Risk Management Framework (AI RMF) 1.0, released in January 2023, provides voluntary guidance for organizations that develop or use AI. Its four functions are Govern, Map, Measure and Manage. They are connected activities, not a mandatory sequence or a product certification (NIST, 2023).

For an SMB, an illustrative application is:

• Govern: Assign ownership and establish acceptable-use rules.
• Map: Identify the AI tools, users and information involved.
• Measure: Review usage, violations and control effectiveness.
• Manage: Prioritize action and adjust controls as the risks change.

ISO/IEC 42001:2023, Information technology: Artificial intelligence: Management system, is the first international AI management system standard. It sets requirements for establishing and continually improving an organization's AI management system. Clause 5.2 covers AI policy, including top management's responsibility to establish it (ISO/IEC, 2023).

The two references serve different purposes: NIST AI RMF provides risk-management guidance, while ISO/IEC 42001 defines management-system requirements. Neither means that buying an AI security product makes an organization compliant.

OWASP's LLM risk categories add a technical layer by describing vulnerabilities such as prompt injection. A GenAI security program can use these resources together: management frameworks establish responsibilities and objectives, while technical guidance informs the controls.

For a smaller organization, start with an AI usage policy that people can follow. Use the evaluation checklist to connect policy to application access, interaction inspection, reporting and the responsibilities agreed with your MSP.

Acronis GenAI Protection helps MSPs govern and secure employee GenAI use within Acronis Cyber Platform (formerly Acronis Cyber Protect Cloud).

The service brings the three capability areas below into the Acronis platform instead of adding a separate stand-alone AI security console. Its role here is to protect employee GenAI use, not to provide a complete security system for proprietary models or custom AI applications (Acronis, 2026).

Acronis GenAI Protection combines usage visibility and access controls for supported GenAI applications.

Browser-based visibility helps MSPs review activity in supported browser sessions. Application access control separately governs supported web-based and desktop GenAI applications through allowlists or blocklists, without relying on URL filtering. User-level monitoring and reporting tie recorded activity to users as well as devices (Acronis, 2026).

Visibility shows which applications are being used, access control determines whether they may be used and content inspection examines supported interactions. Controlling access to an application does not establish content-inspection coverage for every interaction within it.

Within supported AI interactions, Acronis GenAI Protection can inspect prompts and file uploads for sensitive information.

Examples include personally identifiable information (PII) and protected health information (PHI). Administrators can configure data-protection actions to detect or block and review recorded events. Coverage depends on the supported application, interaction type and file format; file-upload inspection does not mean every file or upload method is supported (Acronis, 2026).

Within its supported protection scope, Acronis GenAI Protection detects and blocks harmful prompts, including prompt injection attempts.

Providers can configure these controls and review prompt-injection events through the Acronis console. The controls reduce exposure to malicious prompting, but they do not make prompt injection impossible or replace the need to limit access and review sensitive actions (Acronis, 2026).

Centralized policies, a unified multitenant platform and partner- and client-level reporting help MSPs operate these controls across customer environments. For the business, that supports managed GenAI governance within its provider's existing Acronis environment (Acronis, 2026).

Key takeawaysShadow AI, GenAI data leakage and prompt injection need related but distinct controls.Match protection to actual applications, interactions and endpoint requirements.Use detect-only and block modes according to risk, not a universal rollout rule.Combine policy ownership with enforcement and reporting, whether IT or an MSP runs the service.

AI security buying decisions come down to how the business uses AI, what information is involved and who will operate the controls.

A business can face AI security risks simply because employees use external GenAI tools. It may need controls over unapproved use, sensitive submissions and harmful prompts without needing model-development security tools.

An existing DLP tool may cover some GenAI interactions, depending on the product and configuration. Verify the applications, prompts and uploads it can inspect or block, and check separately for shadow AI discovery and prompt injection detection.

A business can prohibit known high-risk AI activity while assessing approved, lower-risk workflows. Choose restrictions according to data obligations and available controls, rather than assuming every rollout requires either blanket blocking or detect-only operation.

A managed AI security service should define policy maintenance, event review, exception handling and client reporting. It should also document coverage and explain which decisions remain with the business. A software subscription alone does not establish those operational responsibilities.

No security product should be treated as a guarantee against prompt injection. Detection and blocking can reduce risk, but application permissions and human review of high-risk actions still matter. Protection should be layered rather than dependent on a single filter (OWASP Top 10 for LLM Applications, 2025).

Using NIST AI RMF does not require certification; it is voluntary risk-management guidance. ISO/IEC 42001 defines an AI management system against which an organization can pursue certification. Applying useful governance practices and obtaining certification are different decisions (NIST, 2023; ISO/IEC, 2023).

Explore Acronis GenAI Protection to see how your business and its MSP can manage GenAI usage, sensitive-data exposure and harmful prompts through the Acronis platform.