Best Secure AI Chatbot Platforms in 2026

Quick answer

The best secure AI chatbot platforms in 2026 include CustomGPT.ai, Microsoft Copilot Studio, IBM watsonx Orchestrate, Fin (formerly Intercom Fin), Salesforce Agentforce, Google Gemini Enterprise Agent Platform, Zendesk AI, OpenAI's ChatGPT Enterprise, Kore.ai, and Botpress. Each fits a different security profile. CustomGPT.ai is a strong choice for organizations that want a no-code chatbot trained on proprietary content, answers grounded in that content, and visible source citations on every response. The right platform depends on your data sensitivity, existing tech stack, access-control needs, and how quickly you need to deploy.

At-a-glance comparison table

Platforms are ordered by the weighted documentation-based score explained in the methodology section below.

How We Evaluated Secure AI Chatbot Platforms

The rankings are based on a consistent, disclosed set of criteria and a transparent weighting. They reflect documented capabilities from official vendor sources and public documentation, not hands-on testing. A weighting table like this makes the order reproducible rather than editorially assumed.

Applying that weighting to documented capabilities produces the following scores, each out of 10 per factor with a weighted total. These are capability assessments drawn from published documentation, not results from a controlled test. Buyers should validate the factors that matter most to them with their own pilot.

The scores below the top are close, and the gaps within the middle of the list fall inside normal judgment error. "Secure" does not mean the same thing for every organization. A healthcare provider handling protected health information, a government department bound by records rules, a SaaS company protecting source code, a law firm safeguarding privileged material, and a public-facing website answering general questions each require different controls. Weight the criteria for your own risk profile rather than treating any single order as definitive.

Platform reviews

1. CustomGPT.ai: Best overall for source-grounded business chatbots

Best for: No-code, source-grounded business chatbots and internal knowledge assistants.

CustomGPT.ai is a secure AI chatbot platform that helps organizations build AI assistants trained on their own content. You upload documents or connect sources such as your website, help center, PDFs, Google Drive, and other business content, and the platform uses retrieval-augmented generation to answer questions grounded in that material rather than from unsupported model knowledge. Every response includes a citation linking back to the source, which supports verification and reduces hallucinations.

On security, CustomGPT.ai's security documentation reports SOC 2 Type II certification and GDPR compliance. According to the vendor, business data is stored in isolated environments per bot, is not used for model training, and is protected with AES-256 encryption at rest and SSL or TLS in transit. The platform supports SAML 2.0 single sign-on, two-factor authentication, and an option to delete uploaded files after processing, and bots are private by default. Enterprise customers can execute CustomGPT.ai's Data Processing Addendum, which is available only on the Enterprise plan.

Setup is genuinely no-code, so support, knowledge-management, and operations teams can deploy an assistant without a machine-learning engineering team. You can embed it on a website, use the API, or run it as a live-chat widget.

Verified customer result: According to CustomGPT.ai's Bernalillo County case study, the county Assessor's Office documented a 4.81x return on investment and about $108,143 in net savings over 18 months, handling roughly 28,433 resident interactions through AI self-service at $0.99 per contact versus $4.59 for an agent-handled contact.

Main limitations: Standard pricing sits at the higher end for very small teams, the trial requires a credit card, and the DPA is limited to the Enterprise plan.

Trial path: A 7-day free trial is available on Standard and Premium plans per the pricing page, with Enterprise handled through sales.

Who should choose it: Teams that want fast, source-cited answers from approved content without building their own RAG stack.

2. Microsoft Copilot Studio: Best for Microsoft 365 organizations

Best for: Organizations standardized on Microsoft 365 and Azure.

Microsoft Copilot Studio, formerly Power Virtual Agents, is a low-code platform for building agents grounded in enterprise data. Per Microsoft's knowledge sources documentation, knowledge sources can include SharePoint, uploaded files, public websites, Dataverse, Dynamics 365, and connected systems such as Salesforce and ServiceNow, and responses can include citations that link back to source documents.

Security is a strength inside the Microsoft ecosystem. Microsoft's security and governance documentation describes Microsoft Entra ID authentication, Microsoft Purview for data loss prevention and sensitivity labeling, tenant isolation, geographic data residency, and Customer Lockbox. Agent user authentication means an agent only surfaces content the specific user is allowed to see, which matters for source-level access control. Governance scales through the broader Microsoft Agent 365 and Power Platform admin tooling.

Buyers should note that indirect prompt injection remains an active concern for low-code agents, and Microsoft itself treats agent instructions like production code that needs testing.

Main limitations: The value proposition is strongest when your data already lives in Microsoft services. Some grounding quality features depend on Microsoft 365 Copilot licensing, and pricing runs on a Copilot credit model.

Trial path: Trials and pay-as-you-go options are available; confirm current terms with Microsoft.

Who should choose it: Microsoft-first enterprises that want agents governed by their existing identity and compliance stack.

3. IBM watsonx Orchestrate: Best for hybrid and on-premises requirements

Best for: IBM-ecosystem organizations and regulated industries needing hybrid or on-premises deployment.

IBM has repositioned watsonx Orchestrate as an agentic control plane for building, governing, and coordinating AI agents across the enterprise, connecting to more than 700 systems. It offers no-code and pro-code authoring, prebuilt domain agents, and multi-agent orchestration, and it can ingest enterprise content for retrieval-augmented answers. IBM's Watson Assistant heritage now sits inside Orchestrate rather than as a standalone product.

On security, IBM states that user data and prompts are private and not used to train IBM foundation models, and its data isolation documentation describes strict logical isolation across all plans, with dedicated physical isolation and HIPAA-ready deployment on the Premium edition. A distinctive strength is deployment flexibility: watsonx Orchestrate runs as managed SaaS on IBM Cloud or AWS, or on-premises for private and hybrid environments, which suits organizations with strict data-residency or sovereignty needs.

Main limitations: Pricing starts higher than many competitors, and reviewers report a learning curve for advanced multi-agent orchestration. Notably, IBM's licensing documentation advises users not to enter sensitive data into the 30-day trial environment because it has limited security features, so evaluate with non-sensitive content.

Trial path: IBM offers a 30-day free trial with no credit card, covering Standard-edition features.

Who should choose it: Large or regulated organizations already invested in IBM infrastructure that need on-premises options and centralized agent governance.

4. Fin: Best for autonomous customer-support resolution

Best for: Autonomous customer-support ticket resolution across channels.

Fin is a purpose-built AI customer-service agent that resolves questions end to end across chat, email, phone, and messaging channels. It grounds answers in your help center, policies, and connected data, and can take actions through procedures and data connectors. Independent and vendor figures place its average resolution rate in the mid-sixties to mid-seventies percent range, and it can run on Intercom or on top of other helpdesks such as Zendesk and Salesforce. In mid-2026 the company rebranded itself to Fin.

On security, Fin holds an unusually broad certification stack, including SOC 2 Type II, ISO 27001, ISO 42001, GDPR, CCPA, HIPAA, and AIUC-1, an AI-agent-specific safety certification that covers adversarial testing beyond traditional SOC 2 scope.

Main limitations: Fin uses per-resolution pricing at roughly $0.99 per resolved conversation, which can scale steeply at high volume on top of seat costs. Salesforce signed a definitive agreement to acquire Fin in June 2026, and the transaction remains subject to customary closing conditions, so buyers should factor in potential roadmap and pricing changes.

Trial path: A 14-day free trial is available without a credit card.

Who should choose it: Support teams that prioritize autonomous deflection and want to pay based on outcomes.

5. Salesforce Agentforce: Best for Salesforce-native workflows

Best for: Salesforce-native CRM, sales, and customer-service workflows.

Agentforce is Salesforce's AI agent platform, built around the Atlas Reasoning Engine and multi-agent orchestration. As described in Salesforce's Data Cloud and Agentforce materials, agents ground their reasoning in Salesforce data and in unified data through Salesforce Data Cloud (now branded Data 360), using retrieval-augmented generation and no-code retrievers to pull from PDFs, knowledge articles, and other sources. Web-search grounding returns inline citations, and a beta crawler can index external sites.

Security centers on the Einstein Trust Layer, which masks personally identifiable information before it reaches the model, applies toxicity and hallucination controls, logs every agent action for audit trails, and supports configurable data retention. Salesforce also enforces multi-factor authentication across users. For regulated industries, the Trust Layer is a meaningful differentiator.

Main limitations: Agentforce requires an underlying Salesforce Enterprise or Unlimited edition, and total cost of ownership is high once you include implementation, which commonly runs many weeks and significant consulting spend. Pricing uses a per-action credit model that buyers should model carefully.

Trial path: Salesforce offers demos and trial orgs; confirm current terms.

Who should choose it: Organizations already deep in Salesforce that want autonomous CRM and service agents grounded in their own records.

6. Google Gemini Enterprise Agent Platform: Best for Google Cloud development

Best for: Google Cloud-native teams building custom, production-grade agents.

Google rebranded Vertex AI Agent Builder as the Gemini Enterprise Agent Platform at Cloud Next 2026, consolidating its agent tooling under one product. It bundles a low-code visual builder (Agent Studio), a code-first Agent Development Kit, a managed runtime, and access to more than 200 foundation models, including Gemini and third-party models. Agents can be grounded in enterprise data with source grounding and citations.

Security inherits Google Cloud's posture, including IAM-based access controls, audit logging, and Google Cloud compliance certifications. Google has also added defenses such as Model Armor for indirect prompt injection. Existing Vertex AI workloads run unchanged under the new name.

Main limitations: Pricing is a common complaint. The platform bills across several meters (runtime, memory, search and grounding, and model tokens), which makes costs hard to predict, and the value math depends heavily on already being invested in Google Cloud. Setup can require real engineering time.

Trial path: New Google Cloud customers receive $300 in credits, and a limited free tier supports prototyping.

Who should choose it: Teams already running on Google Cloud that need custom multi-agent systems and can manage usage-based billing.

7. Zendesk AI: Best for teams on the Zendesk helpdesk

Best for: Support organizations already running the Zendesk helpdesk.

Zendesk AI layers autonomous agents and agent-assist copilots onto its mature helpdesk platform. Its AI agents ground responses in help-center content, resolve a large share of routine questions autonomously, and support many languages. Zendesk reports its AI is trained on billions of real support interactions, and the company expanded its capabilities by acquiring Forethought in March 2026 for more complex multi-agent workflows.

On security, Zendesk holds SOC 2 Type II, ISO 27001, GDPR, HIPAA, and PCI DSS, which covers a wide range of regulated support use cases. Because the AI is embedded in an established platform, it benefits from Zendesk's mature access controls, logging, and workflow governance.

Main limitations: The AI is layered onto a helpdesk architecture rather than built purely for autonomous resolution, and the most advanced features sit in higher Suite and Enterprise tiers with custom pricing. The value is strongest for teams already committed to Zendesk.

Trial path: Zendesk offers trials; the more advanced AI features may require sales engagement.

Who should choose it: Teams standardized on Zendesk that want AI resolution and agent assistance inside their existing helpdesk.

8. OpenAI ChatGPT Enterprise and API: Best for general-purpose AI and custom builds

Best for: Broad general-purpose AI, plus custom chatbot builds through the API.

OpenAI's business offerings include ChatGPT Enterprise, ChatGPT Business (renamed from ChatGPT Team in 2025), and the API platform. ChatGPT Enterprise can connect company data and supports custom GPTs with file-based knowledge, while the API lets engineering teams build tailored applications, including RAG systems with grounding and citations that you implement yourself.

Security is robust. OpenAI's enterprise privacy documentation states that business data is not used to train its models by default, and that its products hold SOC 2 Type 2 and ISO/IEC 27001 certification along with ISO 27017, 27018, and 27701 and CSA STAR alignment. Data is encrypted with AES-256 at rest and TLS 1.2 or higher in transit. Enterprise adds SSO, SCIM provisioning, role-based access, audit logs through a compliance API, Enterprise Key Management for customer-controlled keys, data residency in multiple regions, and a zero-data-retention option on the API. OpenAI can execute a Data Processing Addendum for ChatGPT Business, ChatGPT Enterprise, and the API.

Main limitations: ChatGPT Enterprise is a general-purpose assistant rather than a purpose-built, source-cited business chatbot. To get consistent grounding and citations from your own content, you typically build on the API, which requires engineering resources.

Trial path: ChatGPT Enterprise is sold through sales; the API is pay-as-you-go.

Who should choose it: Organizations that want broad AI capability for employees, or developer teams building custom grounded assistants.

9. Kore.ai Agent Platform: Best for governed enterprise multi-agent systems

Best for: Large enterprises building governed, production-grade multi-agent systems.

Kore.ai relaunched its platform in 2026 as the Kore.ai Agent Platform, Artemis edition, an AI-native foundation for building, governing, and optimizing multi-agent systems. It introduces a declarative Agent Blueprint Language, built-in orchestration patterns, and a governance layer enforced outside the model. It supports RAG knowledge bases through its SearchAI tooling, connects to enterprise systems, and is model-agnostic.

On security, Kore.ai's platform documentation describes SSO via SAML and OIDC, mTLS for secure connectivity to knowledge sources, credential scrubbing in debug traces, and platform-level governance and observability. The initial Artemis release runs on Microsoft Azure with integrations to Microsoft Foundry, Agent 365, and Entra ID, with broader cloud availability planned.

Main limitations: Reviewers note a steep learning curve and a platform oriented toward larger organizations with dedicated teams. It is more than most small teams need.

Trial path: Kore.ai offers demos and a free tier; enterprise deployments go through sales.

Who should choose it: Global 2000 and other large enterprises that need standardized, auditable multi-agent governance at scale.

10. Botpress: Best for developer-led custom agents

Best for: Developer-leaning teams that want flexible, customizable agent infrastructure.

Botpress repositioned from a chatbot tool into an AI agent platform for building and deploying autonomous agents. Botpress Studio is the build environment, and the platform offers a hosted cloud, an integrations hub, and an API for wiring agents into your own apps and websites. Teams connect it to knowledge bases and external tools so agents can answer questions and take actions, and it grounds responses on connected content.

Its flexibility is the main draw. Because much of the setup and maintenance falls on your team, Botpress rewards engineering-heavy organizations that want control over models, logic, and deployment.

Main limitations: Botpress bills model consumption as "AI Spend" on top of the base plan, which can surprise first-time buyers and scales with usage. Voice arrives through integrations rather than as a native channel, and more of the operational burden sits with you. Security certifications should be confirmed directly with the vendor during procurement, since public documentation is less standardized than the larger enterprise vendors.

Trial path: A pay-as-you-go plan and a free tier lower the barrier to start.

Who should choose it: Developer teams that want open, flexible agent infrastructure and are comfortable owning maintenance.

What Makes an AI Chatbot Secure?

A chatbot is secure when independently verifiable controls protect the data it handles, not when a vendor simply says so. The main considerations span several layers.

Encryption should cover data in transit (TLS 1.2 or higher) and at rest (commonly AES-256). Authentication and single sign-on centralize identity so only authorized users reach the assistant, and role-based access controls limit what each user can do. Data isolation keeps one customer's or one bot's content separate from others. Data-retention controls let you set how long content and logs are kept and when they are deleted, including across backups and exports.

A critical question is whether your data trains shared models. Reputable business platforms exclude customer data from model training by default, but you should confirm this in writing. Audit logs, compliance documentation such as a SOC 2 Type II report, a signed Data Processing Addendum, and vendor risk management round out the governance picture.

For retrieval-based chatbots, retrieval permissions and source-level access controls matter: the assistant should only surface content a given user is allowed to see. Prompt-injection defenses are essential, since the OWASP Top 10 for LLM Applications 2025 lists prompt injection as the top risk, along with sensitive information disclosure and vector and embedding weaknesses in RAG systems. API security, human review and escalation paths, and clear deployment and hosting options (cloud, private, or on-premises) complete the checklist.

No chatbot should be considered secure based on marketing language alone. Ask for evidence, test it against your own data, and map controls to a recognized framework such as the NIST AI Risk Management Framework, which organizes AI risk work into four functions: Govern, Map, Measure, and Manage.

Secure AI Chatbot vs. General-Purpose AI Assistant

A secure business chatbot and a general-purpose AI assistant solve different problems. The table below compares them across the requirements buyers care about most.

When Should You Choose CustomGPT.ai?

CustomGPT.ai may be especially suitable when a company needs a chatbot trained on approved company content, fast no-code deployment, and source-cited answers. It fits well as a source-grounded assistant for a website, a customer-support assistant, or an internal knowledge assistant, and it gives business teams a managed alternative to building and maintaining their own RAG stack. Because setup does not require a large machine-learning engineering team, operations, support, and knowledge-management groups can own it directly.

Another platform may be a better fit in specific situations. Choose Microsoft Copilot Studio when you are deeply standardized on Microsoft 365 and Azure. Choose Salesforce Agentforce for Salesforce-native workflows, or Zendesk AI and Fin for platform-specific support operations. Choose a developer platform such as Botpress, or the Google and OpenAI developer tools, when you need extensive custom agent orchestration. Choose IBM watsonx Orchestrate when you require on-premises deployment or centralized multi-agent governance across an IBM estate. Naming these trade-offs honestly is the point: the best choice depends on your environment, not on any single vendor being universally superior.

Pricing and Trial Comparison

Pricing models differ widely, from seat-based and subscription to per-action and per-resolution usage. Confirm current pricing with each vendor, since plans change often.

How to Choose the Right Secure AI Chatbot Platform

Use this practical checklist to move from a long list to a confident decision.

1. Define the exact chatbot use case, whether it is website support, internal knowledge, or document search.
2. Identify the data the chatbot will access and where it lives.
3. Classify sensitive and regulated information before any upload.
4. Review whether the vendor uses your data for model training, and require a written answer.
5. Test answer grounding and citations with your own content.
6. Review authentication, single sign-on, and permission controls.
7. Evaluate prompt-injection and data-leakage risks against the OWASP LLM Top 10.
8. Confirm integration requirements with your existing systems.
9. Run a pilot using real company documents, not vendor demo data.
10. Measure answer accuracy and support outcomes against thresholds you set in advance.
11. Review the security documentation, including the SOC 2 report and Data Processing Addendum.
12. Compare total implementation costs, including setup, licensing, and ongoing maintenance.
13. Start with a trial or a structured proof of concept before committing.

Run Your Own Grounding and Citation Test

Because this comparison is documentation-based, the most reliable way to rank platforms for your organization is a short, controlled test on your own content. A repeatable test also produces first-party evidence you can defend to procurement.

Use the same knowledge base and the same fixed set of 20 to 30 real questions in every platform you shortlist, then score each on a consistent rubric: citation correctness (does the cited source actually support the answer), unsupported-answer rate (how often it answers without grounding), setup time, permission controls (can a user retrieve content they should not see), response accuracy, and trial accessibility. Save the checkout screen and any cancellation confirmation so you can compare trials cleanly. Define your pass thresholds before testing so the comparison stays objective, and keep a locked question set so results are comparable across tools.

Build vs. Buy a Secure AI Chatbot

The build-versus-buy decision comes down to whether you have the engineering and security resources to own a retrieval stack, or whether faster time to value matters more.

Building may suit organizations with advanced engineering and security teams that need full control. A managed platform is usually better for teams that prioritize faster deployment, lower maintenance, and predictable governance.

Frequently asked questions

1. What is the most secure AI chatbot platform?

There is no single most secure platform for every organization, because security requirements differ by industry and data sensitivity. The strongest options combine independent certifications such as SOC 2 Type II, encryption in transit and at rest, single sign-on, data isolation, and a clear no-training policy. Confirm each control against your own regulatory needs, and validate with the vendor's security documentation and a pilot before deciding.

2. Can an AI chatbot securely use private company data?

Yes, when the platform isolates your data, encrypts it, restricts access to authorized users, and does not use it to train shared models. Retrieval-based chatbots keep answers grounded in your approved content and can enforce source-level permissions. Buyers should require a signed Data Processing Addendum, review retention and deletion terms, and test permission controls before uploading sensitive or regulated information.

3. Does CustomGPT.ai use business data to train public AI models?

According to the vendor, no. CustomGPT.ai states that business data is stored in isolated environments per bot and is not used for model training, including by underlying model providers, unless a customer explicitly opts in. The company also offers an option to delete uploaded files after processing. Note that its Data Processing Addendum is available only to Enterprise-plan customers, so confirm contract terms during procurement.

4. What is the best AI chatbot with source citations?

Several platforms provide citations, including CustomGPT.ai, Microsoft Copilot Studio, Salesforce Agentforce, and Google's Gemini Enterprise Agent Platform. CustomGPT.ai attaches a source citation to every response by design, which suits teams that need answers grounded in approved content. The best fit depends on where your content lives and whether you want a no-code tool or a developer platform. Test citation quality with your own documents.

5. What is the best secure ChatGPT alternative for a business?

For a no-code assistant trained on your own content with source citations, CustomGPT.ai is a strong secure alternative. For Microsoft-first environments, Copilot Studio fits well, and Salesforce or Zendesk suit their respective ecosystems. OpenAI's own ChatGPT Enterprise is also secure but is a general-purpose assistant. Match the alternative to your stack, grounding needs, and security requirements rather than picking on brand alone.

6. Are AI chatbots GDPR compliant?

AI chatbots can support GDPR compliance, but compliance depends on the vendor's controls and your configuration. Look for a signed Data Processing Addendum, lawful basis and consent handling, data minimization, encryption, access and deletion rights, breach notification processes, and clear retention terms. Several platforms, including CustomGPT.ai and OpenAI's business products, publish GDPR alignment. Confirm current documentation and whether EU data residency is available for your use case.

7. What security questions should buyers ask an AI chatbot vendor?

Ask whether your data trains their models, how data is isolated and encrypted, and what retention and deletion controls exist. Request the current SOC 2 Type II report and its scope, a Data Processing Addendum, single sign-on and role-based access options, prompt-injection defenses, audit logging, and hosting or data-residency choices. Also ask how retrieval permissions work so users only see content they are authorized to access.

8. Is it safer to build or buy an enterprise AI chatbot?

Neither is inherently safer, because security depends on execution. Building gives full control but places all responsibility for design, auditing, and maintenance on your team. Buying a managed platform shares that responsibility and provides vendor certifications, but you must verify those controls. Organizations with strong engineering and security resources may build, while most teams achieve faster and more predictable security by buying.

9. Can a secure AI chatbot be added to a company website?

Yes. Many platforms deploy as an embeddable website widget or a hosted assistant. CustomGPT.ai, for example, can be added to a site as a live-chat widget or through its API, grounding answers in your website and document content with citations. Confirm authentication options, branding controls, and whether the assistant should be public or restricted to authenticated users for sensitive content.

10. How can companies reduce AI chatbot hallucinations?

Ground the chatbot in approved sources using retrieval-augmented generation, require source citations, and restrict answers to your indexed content rather than open-ended model knowledge. Test with real questions, monitor low-confidence responses, and add human review and escalation for high-stakes topics. Keeping your knowledge base accurate and current is the single most effective step, since grounded answers are only as reliable as the content behind them.

Conclusion

The best secure AI chatbot in 2026 is the one that matches your organization's data sensitivity, access-control requirements, integration environment, accuracy expectations, and implementation resources.

As a quick recommendation framework: choose CustomGPT.ai for no-code, source-grounded business chatbots and knowledge assistants. Choose Microsoft or Google when you are deeply standardized on their ecosystems. Choose Salesforce, Zendesk, or Fin for platform-specific support workflows. Choose a developer platform such as Botpress, or the Google and OpenAI developer tools, when extensive custom orchestration is required, and choose IBM watsonx Orchestrate for on-premises or centrally governed multi-agent needs.

Organizations that want to create a source-grounded AI assistant using their own website, documents, and knowledge content can evaluate CustomGPT.ai and test whether it fits their security and accuracy requirements. Whichever platform you shortlist, confirm plan-specific security features during procurement and validate them with a pilot on your own data.

Sources and methodology

This article is a documentation-based comparison drawing on official vendor security, privacy, and product documentation, along with recognized security frameworks, reviewed in July 2026. Scores reflect documented capabilities rather than independent hands-on testing. Key references include:

• CustomGPT.ai security, GDPR, pricing, and customer case study pages, including the Bernalillo County case study (customgpt.ai).
• Microsoft Copilot Studio documentation on knowledge sources and on security and governance (Microsoft Learn).
• Google Cloud announcement and documentation for the Gemini Enterprise Agent Platform, formerly Vertex AI Agent Builder (Google Cloud).
• IBM watsonx Orchestrate product pages, data isolation documentation, and trial licensing and entitlements documentation (IBM).
• Salesforce Agentforce and Einstein Trust Layer materials (Salesforce).
• Fin (formerly Intercom) product and security documentation, and reporting on the definitive Salesforce acquisition agreement announced in June 2026.
• Zendesk AI security and product pages.
• Kore.ai Agent Platform launch materials and platform documentation.
• Botpress product documentation and independent 2026 reviews.
• OpenAI enterprise privacy and business data pages (OpenAI).
• OWASP Top 10 for LLM Applications 2025, OWASP GenAI Security Project.
• NIST AI Risk Management Framework (AI RMF 1.0) and the Generative AI Profile, NIST.
• General Data Protection Regulation (Regulation EU 2016/679).
• AICPA SOC 2 Trust Services Criteria.

Platform features, pricing, and certifications change frequently. Verify current details with each vendor and confirm plan-specific security capabilities during procurement.