# Contact Center AI Security Requirements Checklist

> AI security requirements for contact centers: PII redaction, threat mitigation, and vendor assessment. Plura AI supports your compliance readiness.

**Published:** 2026-09-06 | **Updated:** 2026-11-06 | **Author:** plurasync
**URL:** https://www.plura.ai/articles/contact-center-ai-security-requirements
**Type:** post

**Categories:** AI Contact Centers

![Contact Center AI Security Requirements Checklist](https://plura.sites.aigrowthagent.co/wp-content/uploads/sites/75/2026/09/1788600691222-8caaae34356a-1024x572.webp)

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## Content

*Written by: Matt Beucler, CEO, Plura AI | Last updated: September 29, 2026*

## Key Takeaways

- Contact center AI security rests on three pillars: layered technical controls, regulatory alignment, and continuous runtime monitoring mapped to named frameworks such as NIST AI RMF, OWASP LLM Top 10 2025, and the EU AI Act.
- Every production deployment must clear a minimum baseline that includes SSO + MFA + RBAC, TLS 1.3 / AES-256 encryption, tenant isolation, PII/PCI/PHI redaction, prompt-injection defenses, immutable audit logging, and a documented kill switch.
- Each control must be paired with a specific evidence artifact such as access-control matrices, redaction test results, RAG permission models, and SIEM integration documentation that vendors can present to CISOs, procurement teams, and auditors.
- High-risk AI threats such as prompt injection, deepfake voice attacks, and cross-tenant RAG leakage require architectural mitigations like least-privilege tool access, out-of-band confirmation, and per-document permissions.
- Plura AI delivers these controls natively through its FCC-licensed carrier stack and 100% U.S. infrastructure, with a compliance posture that includes SOC 2, HIPAA, SHAKEN/STIR caller ID verification, and real-time DNC/TCPA enforcement.1 Plura supports customer compliance; it does not absolve customers of their own obligations.

[See how Plura maps to these controls in a live environment](https://www.plura.ai/plura-webchat?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent)

## Minimum Baseline Before Production

Before any contact center AI system touches a live customer, it has to clear a fixed set of controls. The list below is that floor. If a vendor cannot demonstrate every item, the deployment is not ready for production use.

- SSO (Single Sign-On) + MFA (Multi-Factor Authentication) + RBAC (Role-Based Access Control)
- Encryption in transit (TLS 1.3) and at rest (AES-256)
- Tenant isolation
- PII (Personally Identifiable Information), PCI (Payment Card Industry), and PHI (Protected Health Information) detection and redaction
- Provider data retention and model training controls
- Independent authorization for every AI tool action
- Prompt injection defenses
- RAG (Retrieval-Augmented Generation) access controls
- Immutable audit logging
- Rate limiting and token quotas
- Human approval for high-risk transactions
- Secure human fallback
- AI-specific incident response plan and documented kill switch
- Adversarial testing across all input channels
- Vendor and subprocessor assessment
- Documented data retention and deletion policy

## How to Read This Requirements Reference

That baseline is only useful if each item can be verified. Each section below follows the same pattern: the control, the framework it maps to, and the evidence to demand from a vendor. Every requirement is testable and can be lifted directly into a procurement document or RFP security questionnaire. This page references the [OWASP Top 10 for LLM Applications 2025 (v2.0, published November 2024)](https://articsledge.com/post/owasp-top-10-for-llm-applications) and the [EU AI Act’s Article 50 application date of August 2, 2026](https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act), framework versions that competing resources have not updated to as of September 2026.

See how Plura maps to these controls in a live environment

## Identity, Access, and Authorization Requirements

**Control:** SSO via SAML or OIDC, MFA on every account that can reach customer data, and RBAC or ABAC (Attribute-Based Access Control) with least privilege enforced. Separate permission tiers for customer, agent, supervisor, admin, and AI service identities. Authorization must be enforced by the downstream API on every invocation. The system prompt is a suggestion the model usually follows, not an enforced security boundary, so it cannot carry authorization, as [OWASP documents](https://genai.owasp.org/llm-top-10/).

**Framework:** [OWASP LLM Top 10 2025, LLM01 (Prompt Injection) and LLM07 (System Prompt Leakage)](https://genai.owasp.org/llm-top-10/); NIST CSF 2.0 PR.AC-4 (Access Permissions and Authorizations).

**Evidence to demand:** Access control matrix, SSO configuration documentation, and an API authorization architecture diagram showing enforcement at the downstream API layer rather than the prompt layer.

## Data Protection and Minimization Requirements

**Control:** TLS 1.3 in transit, AES-256 at rest, tenant isolation, field-level access controls, and tokenization. Data minimization applies at the prompt level. An AI agent that needs account status should receive only that status, not a full CRM record. [NIST AI 600-1 defines Data Privacy risk as “leakage and unauthorized use, disclosure, or de-anonymization of biometric, health, location, or other personal or sensitive data, including data used in training.”](https://casrai.org/guides/nist-ai-rmf-generative-ai-profile)

**Framework:** [NIST AI RMF (NIST AI 100-1)](https://www.nist.gov/itl/ai-risk-management-framework) privacy characteristic and the [NIST AI 600-1 Data Privacy risk category](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf).

**Evidence to demand:** Encryption architecture documentation, tenant isolation test results, and a data minimization policy that specifies what data reaches the model and what is excluded.

## PII, PCI, and PHI Detection and Redaction Requirements

**Control:** Detection and redaction across transcripts, recordings, prompts, logs, embeddings, and the original audio. [Research by AI security firm Lakera found that prompt injection now appears in over 73% of production AI deployments](https://business-standard.com/technology/artificial-intelligence/prompt-injection-to-deepfakes-how-ai-rewrites-rules-of-enterprise-security-126062900556_1.html)3, and sensitive data leakage through conversational output is a documented attack class in voice AI environments. Redaction must cover structured identifiers such as card numbers and SSNs and unstructured sensitive disclosures in natural language output.

**Framework:** HIPAA Security Rule 45 CFR 164.312 (technical safeguards including audit controls and transmission security); PCI DSS Requirement 3.3.1 (prohibition on storing Sensitive Authentication Data after authorization); [NIST AI 600-1 Data Privacy risk category](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf).

**Evidence to demand:** Redaction accuracy test results, redaction coverage documentation specifying which channels and data types are covered, and a sample redacted transcript showing audio-level and text-level redaction.

## AI-Specific Threat Mitigation Requirements

**Control:** Direct and indirect prompt injection defense, sensitive information disclosure prevention, limits on excessive agency, secure tool and plugin design, output validation before downstream execution, and voice spoofing and deepfake shielding. [Pindrop’s 2025 Voice Intelligence and Security Report, which analyzed more than 1.2 billion customer calls, found that deepfake fraud attempt frequency surged more than 1,300% in 2024, jumping from roughly one attempt per month to seven per day.](https://business-standard.com/technology/artificial-intelligence/prompt-injection-to-deepfakes-how-ai-rewrites-rules-of-enterprise-security-126062900556_1.html)3 For high-value transactions, out-of-band confirmation via a verified callback channel reduces voice-only deepfake exposure.

**Framework:** [OWASP LLM Top 10 2025, including LLM01 (Prompt Injection), LLM02 (Sensitive Information Disclosure), LLM05 (Improper Output Handling), and LLM06 (Excessive Agency)](https://genai.owasp.org/llm-top-10/); [NIST AI 600-1 Information Security risk category](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf).

**Evidence to demand:** Prompt injection test results covering both direct and indirect injection vectors, output validation architecture documentation, voice biometrics and deepfake shielding documentation, and adversarial red-team test results across all input channels.

## RAG and Knowledge Base Security Requirements

**Control:** Access-controlled retrieval with document-level permissions enforced at query time. Protection against poisoned documents in the retrieval corpus and strict prevention of cross-tenant retrieval. [OWASP LLM08 (Vector and Embedding Weaknesses) covers vector databases that lack per-tenant isolation, return one customer’s documents to another customer’s query, and allow attackers to craft documents designed to score highly in similarity search.](https://articsledge.com/post/owasp-top-10-for-llm-applications)

**Framework:** [OWASP LLM Top 10 2025, LLM08 Vector and Embedding Weaknesses](https://genai.owasp.org/llm-top-10/).

**Evidence to demand:** RAG access control documentation, a document permission model showing per-document and per-tenant enforcement, cross-tenant isolation test results, and a document provenance tracking policy.

## Logging, Monitoring, and AI-Specific Incident Response Requirements

**Control:** Immutable audit logs that capture prompts, model calls, tool calls, and authorization decisions as structured events. SIEM (Security Information and Event Management) integration, anomalous tool-use detection, and a documented kill switch with a tested procedure. [NIST AI 600-1 control GV-1.5-002 describes policies and procedures for after-action reviews of generative AI system incident response and incident disclosures, to identify gaps and update processes as required.](https://risktemplate.com/blog/2026-04-25-genai-incident-disclosure-content-provenance-nist-ai-600-1)

**Framework:** [NIST AI 600-1 GV-1.5-002](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf); NIST CSF 2.0 DE.CM-08 (Monitoring for Unauthorized Activity).

**Evidence to demand:** Audit log schema showing structured JSON events per tool call, SIEM integration documentation, and an incident response runbook with kill switch procedure and a tested escalation path.

## Vendor and Subprocessor Assessment: The RFP Section

Vendor and subprocessor controls often decide whether a deployment clears legal and security review. Procurement teams need a clear view of where models run, how data flows, and which third parties touch customer information.

The controls to verify:

- Where models run and where data is processed, including domestic versus offshore infrastructure
- Data retention periods and deletion procedures
- Complete subprocessor list including STT (Speech-to-Text), TTS (Text-to-Speech), and LLM providers
- Whether customer data is used to train or fine-tune models
- Geographic processing restrictions
- No-model-training guarantee in the contract, rather than only in documentation
- BAA (Business Associate Agreement) availability for PHI-touching workflows, per HIPAA 45 CFR 164.308(b)(1)
- AI disclosure posture under [EU AI Act Article 50](https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act), which applies from August 2, 2026 and describes how individuals interacting with AI systems are informed from the start of the first interaction, in a clear and distinguishable manner

**Framework:** SOC 2 Type II (AICPA Trust Services Criteria); HIPAA 45 CFR 164.308(b)(1) BAA requirement; [EU AI Act Article 50](https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act).

**Evidence to demand:** SOC 2 Type II report, penetration test summary, complete subprocessor list, model retention policy, BAA availability confirmation, and geographic processing documentation.

![Plura Security & Compliance dashboard highlighting SOC 2, ISO, and GDPR standards with secure trust verification management.](https://cdn.aigrowthmarketer.co/1779339090994-980045ddacd2.png)**Plura Security & Compliance supports SOC 2, ISO, and GDPR standards with trust registration, verification management, and secure AI communications.**

For organizations evaluating platforms against these controls, Plura AI provides a concrete reference point. Plura owns its FCC-licensed carrier stack, so compliance enforcement happens inside the platform before the call is dialed rather than as a bolt-on after it leaves the network. That carrier position is what lets Plura issue branded caller ID and run STIR/SHAKEN (Secure Telephone Identity Revisited / Signature-based Handling of Asserted information using toKENs) authentication on every outbound call. The same layer enforces real-time DNC (Do Not Call) scrubbing, TCPA (Telephone Consumer Protection Act, 47 U.S.C. 227) litigator screening, automated quiet hours, and immutable consent logging on every outbound contact. All of it runs on 100% U.S. infrastructure by architecture. Plura’s compliance posture includes SOC 2, HIPAA, ISO certification, GDPR (Regulation (EU) 2016/679), SHAKEN/STIR caller ID verification, TCPA-related controls, and DNC-related controls.1 Plura supports customer compliance; it does not absolve customers of their own obligations.

![Screenshot of Plura’s fully compliant AI communications platform showing business registration and phone number provisioning workflows for AI Voice, SMS, RCS, and Webchat communication automation.](https://cdn.aigrowthmarketer.co/1779337911454-8c3a9645d906.png)**Plura’s FCC-licensed AI communications platform simplifies compliant business registration and phone number provisioning for AI Voice, SMS, RCS, and Webchat workflows.**

Plura’s [AI voice agent](https://plura.ai/ai-voice-demo?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent) and [AI predictive dialer](https://plura.ai/ai-predictive-dialer?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent) both inherit the carrier-level compliance foundation. The platform’s [managed workflows](https://plura.ai/managed-workflows?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent) include explicit guardrails such as BATNA (Best Alternative to a Negotiated Agreement) floors and ceilings, human escalation triggers, and sensitive-data redaction at the field level. [CRM integration](https://plura.ai/integrations?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent) and [conversation intelligence](https://plura.ai/business-intelligence?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent) feed audit-ready reporting without manual log assembly. Teams can review [pricing and plans](https://plura.ai/pricing?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent) or run projections with the [ROI calculator](https://plura.ai/calculator?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent).

See these controls running in a live environment

## Frequently Asked Questions

### What Security Requirements Should a Contact Center AI Vendor Meet?

A contact center AI vendor should meet the minimum baseline before any production deployment. That baseline includes SSO, MFA, and RBAC with least privilege, TLS 1.3 encryption in transit and AES-256 at rest, tenant isolation, and PII, PCI, and PHI detection and redaction across all channels including original audio. It also includes a documented data retention and deletion policy, independent authorization for every AI tool action enforced at the API layer, prompt injection defenses covering both direct and indirect vectors, and RAG access controls with per-document permissions. Vendors should also provide immutable audit logging with SIEM integration, rate limiting and token quotas, human approval gates for high-risk transactions, a documented kill switch, adversarial testing results, and a complete subprocessor list with BAA availability for PHI-touching workflows.

The control-to-standard-to-evidence pattern separates a defensible requirements set from a narrative checklist. Every control should map to a named framework such as NIST AI RMF, OWASP LLM Top 10 2025, HIPAA, PCI DSS, or SOC 2 Type II and be paired with a specific artifact the vendor must produce. That pattern is what makes the requirements set defensible.

### How Do You Prevent Prompt Injection in a Contact Center AI Agent?

[OWASP LLM01 (Prompt Injection) has held the top position in both the 2023 and 2025 editions of the OWASP Top 10 for LLM Applications](https://articsledge.com/post/owasp-top-10-for-llm-applications) because full prevention is structurally difficult. Effective mitigations are architectural rather than filter-based. Treat all retrieved and user-supplied content as untrusted data rather than instructions. Enforce strict input and output schemas. Separate system instructions from untrusted content in distinct channels. Apply least privilege to every tool or API the model can call so a compromised agent cannot reach systems beyond its task scope. Require human confirmation before high-impact actions such as payments, account modifications, or external communications. Keyword and regex-based filters can be trivially bypassed and should not serve as the primary defense. Adversarial red-team testing should cover all input channels, including retrieved documents and audio input, not only the chat interface.

### What Does NIST AI RMF Require for Contact Center AI?

The [NIST AI Risk Management Framework (NIST AI 100-1) is organized around four core functions: Govern, Map, Measure, and Manage](https://systemprompt.io/guides/ai-risk-management). For contact center AI, the Govern function covers who approves autonomous tool access and at what risk tier, how the organization maintains an inventory of every agent and the tools it can call, and how it defines an AI-specific incident response policy. The Map function covers documentation of each agent’s data reach and possible harms and classification of which inputs are untrusted. The Measure function covers capture of every tool call, prompt, and permission decision as structured logs with SIEM ingestion. The Manage function covers restrictions on which tools an agent may load, scanning of tool-call payloads before execution, caps on tool-call rate per agent and tenant, and reassessment of residual risk on a fixed cadence.

[NIST AI 600-1, the Generative AI Profile published July 2024, extends the base framework to 12 generative AI risk categories.](https://casrai.org/guides/nist-ai-rmf-generative-ai-profile) The two most directly relevant to contact center AI are Data Privacy, which covers leakage and unauthorized use, disclosure, or de-anonymization of personal or sensitive data, and [Information Security, which covers lowered barriers for offensive cyber capabilities and an increased attack surface for model weights and training data](https://casrai.org/guides/nist-ai-rmf-generative-ai-profile). [NIST AI 600-1 is voluntary guidance, not law](https://regulations.ai/regulations/RAI-US-NA-GENERAT-2024), and US federal procurement increasingly references it while auditors treat it as a baseline for reasonable practice.

### Which Compliance Frameworks Apply to Contact Center AI?

Multiple frameworks apply simultaneously, and each covers a different layer of risk. NIST AI RMF (NIST AI 100-1) and NIST AI 600-1 provide the AI-specific risk management structure. NIST CSF 2.0 provides the cybersecurity control layer, including PR.AC-4 (Access Permissions and Authorizations) and DE.CM-08 (Monitoring for Unauthorized Activity). [The OWASP Top 10 for LLM Applications 2025 covers the ten most critical AI application security risks, from LLM01 Prompt Injection through LLM10 Unbounded Consumption.](https://articsledge.com/post/owasp-top-10-for-llm-applications) [The EU AI Act Article 50, which applies from August 2, 2026, describes transparency obligations on AI systems that directly interact with natural persons, with fines that can reach up to 15 million euros or 3% of total worldwide annual turnover for non-compliance.](https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act) HIPAA (45 CFR Parts 160, 162, 164) applies to systems that process electronic protected health information, including AI voice agents that conduct patient intake. [PCI DSS Requirement 3.3.1 addresses storage of Sensitive Authentication Data after authorization, and Requirement 10.5.1 addresses tamper-proof, timestamped audit logs retained for at least 12 months.](https://predictionguard.com/blog/pii-detection-financial-services-pci-glba) SOC 2 Type II (AICPA Trust Services Criteria) covers the infrastructure layer over a defined audit period. [TCPA (47 U.S.C. § 227) and FCC implementing regulations apply to AI-generated voice calls.2](https://prompthalo.ai/feeds/blog/voice-ai-platforms-advanced-security-compliance-features-2025) Organizations should consult qualified counsel to determine which frameworks apply to a specific deployment.

### How Do You Secure Voice AI Against Deepfake and Spoofing Attacks?

Voice biometrics relies on the unique acoustic characteristics of a voice but can be spoofed by a convincing deepfake, so it performs best as one factor in a multi-factor system rather than a standalone gate for high-value transactions. [The same Pindrop report cited earlier found the sharpest increases in financial services, where synthetic voice attacks rose 475% at insurance companies and 149% at banks.](https://business-standard.com/technology/artificial-intelligence/prompt-injection-to-deepfakes-how-ai-rewrites-rules-of-enterprise-security-126062900556_1.html)4 For high-value transactions including payment authorization, account modifications, and identity verification, out-of-band confirmation via a verified callback number or secondary channel approval provides an additional control. Deepfake shielding documentation and voice biometrics test results should be requested as evidence artifacts from any vendor that handles sensitive account actions over voice.

### What Evidence Should You Demand from a Contact Center AI Vendor?

The complete evidence artifact list for a contact center AI vendor assessment includes:

- SOC 2 Type II report that covers the audit period rather than a point-in-time attestation
- Penetration test summary dated within 12 months
- Complete subprocessor list including STT, TTS, and LLM providers
- Model retention policy and a no-model-training guarantee
- BAA availability confirmation for PHI-touching workflows
- Access control matrix with separate permission tiers per role
- Encryption architecture documentation that covers TLS 1.3 in transit and AES-256 at rest
- Prompt injection test results covering direct and indirect vectors
- Redaction accuracy test results and coverage documentation
- RAG access control documentation and cross-tenant isolation test results
- Audit log schema showing structured events per tool call
- Incident response runbook with kill switch procedure
- Geographic processing documentation confirming infrastructure location

### How Do You Handle PII and PHI Redaction in Contact Center AI?

Redaction must cover every layer of the data flow, including transcripts, recordings, prompts, logs, embeddings, and the original audio. Text-only redaction is insufficient because PHI and PII can be reconstructed from audio recordings even when the derived transcript is redacted. The HIPAA Security Rule at 45 CFR 164.312 describes technical safeguards including audit controls, access controls, and transmission security for systems that handle electronic protected health information.2 PCI DSS Requirement 3.3.1 addresses storage of Sensitive Authentication Data after authorization. NIST AI 600-1’s Data Privacy risk category applies to generative AI systems that process personal or sensitive data. Redaction should cover both structured identifiers such as card numbers, SSNs, and account credentials and unstructured sensitive disclosures in natural language output. AI voice agents can be induced to reproduce sensitive data through conversational inference sequences that traditional data loss prevention tools cannot intercept.

### What Is an AI-Specific Incident Response Plan for Contact Centers?

An AI-specific incident response plan for contact centers extends the standard cybersecurity incident response process with controls tailored to generative AI behavior. [NIST AI 600-1 control GV-1.5-002 describes policies and procedures for after-action reviews of generative AI system incident response and incident disclosures, to identify gaps and update processes as required.](https://risktemplate.com/blog/2026-04-25-genai-incident-disclosure-content-provenance-nist-ai-600-1) The plan should define AI-specific incident categories such as harmful outputs, prompt injection exploitation, unauthorized tool calls, data leakage through conversational output, anomalous tool-use patterns, and model drift. Each category needs a severity level, escalation path, and response playbook.

The kill switch procedure must be documented and tested. Immutable audit logs are the forensic foundation for any post-incident reconstruction. They must capture prompts, model calls, tool calls, and authorization decisions as structured JSON events. SIEM integration ensures that AI-specific events are visible in the same platform where security teams handle other alerts. The plan should also address third-party AI service incidents, including what data was sent to the provider, what the provider retained, and what downstream systems relied on the output.

## Conclusion: Turning Requirements Into Vendor Decisions

Security and compliance teams need more than a narrative checklist. A requirements document that lists “encryption” without specifying TLS 1.3 and AES-256, or “compliance” without naming 45 CFR 164.312 or OWASP LLM01, does not give them enough detail to evaluate a platform.

The control-to-standard-to-evidence pattern in this reference turns high-level goals into concrete vendor questions. Every control names the framework it maps to and the artifact a vendor must produce, so every section can be lifted into an RFP or security questionnaire without modification.

For organizations that need a platform already aligned with these controls, Plura AI provides a practical starting point. Plura owns its FCC-licensed carrier stack, enforces compliance-related controls inside the platform before dial, and runs on 100% U.S. infrastructure by architecture. That combination addresses three common failure modes in contact center AI security: compliance controls added after deployment, offshore infrastructure exposure, and authorization enforced only at the prompt layer. Plura’s compliance posture includes SOC 2, HIPAA, ISO certification, GDPR, SHAKEN/STIR caller ID verification, TCPA-related controls, and DNC-related controls. Plura supports customer compliance; it does not absolve customers of their own obligations.

Discuss your requirements with Plura’s team

1 Plura AI maintains SOC 2, HIPAA, ISO, and GDPR posture as part of its platform infrastructure. References to compliance frameworks in this article describe Plura’s platform capabilities and do not constitute a guarantee that any customer using Plura will themselves be compliant with applicable laws or standards. Customers remain solely responsible for their own regulatory obligations, certifications, consent management, recordkeeping, and the claims they make to their own end users. Consult qualified legal counsel for guidance specific to your use case.

2 This article describes regulatory frameworks at a general level and does not constitute legal advice. Laws and regulations vary by jurisdiction, change over time, and apply differently depending on facts and circumstances. Readers should consult qualified legal counsel before making compliance decisions.

3 Performance figures, customer outcomes, and industry statistics referenced in this article are drawn from cited third-party sources or Plura customer case studies. Individual results vary based on implementation, use case, industry, audience, and execution. Past or aggregate performance is not a guarantee of future results.

4 References to third-party products, services, companies, or research are made for informational and comparative purposes only. Plura AI is not affiliated with, endorsed by, or sponsored by any third party named in this article unless explicitly stated. Trademarks and product names referenced remain the property of their respective owners.

This article is provided for informational purposes only and reflects Plura AI’s understanding at the time of publication. Product capabilities, integrations, and specifications are subject to change. For the most current information, visit plura.ai.

This article was produced with the assistance of AI tools and reviewed by Plura AI prior to publication.

## Read Next

- [Contact Center AI Compliance: A Six-Layer Control Framework](https://www.plura.ai/articles/contact-center-ai-compliance-2026/?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent)
- [The AI Receptionist Compliance Checklist](https://www.plura.ai/articles/ai-receptionist-compliance-guide/?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent)
- [Contact Center AI Features: The Complete Breakdown](https://www.plura.ai/articles/contact-center-ai-features/?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent)
- [AI Voice Agent Compliance: Engineering Controls That Matter](https://www.plura.ai/articles/ai-voice-agent-compliance/?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent)
- [AI for Insurance Contact Centers: Workflows and QA](https://www.plura.ai/articles/contact-center-ai-insurance/?utm_term=contact-center-ai-security-requirements&utm_campaign=ai_growth_agent)

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## Structured Data

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- **Headline:** Contact Center AI Security Requirements Checklist
- **Description:** AI security requirements for contact centers: PII redaction, threat mitigation, and vendor assessment. Plura AI supports your compliance readiness.
- **DateModified:** 2026-09-29T09:08:01.073Z
- **Image:** https://cdn.aigrowthmarketer.co/1779339090994-980045ddacd2.png, https://cdn.aigrowthmarketer.co/1779337911454-8c3a9645d906.png
- **InLanguage:** en-US
  **Person:**

  - **Name:** Matt Beucler
  - **JobTitle:** CEO
  - **Image:** https://cdn.aigrowthmarketer.co/1778629493777-adf5cc534410.png
  - **Url:** https://www.plura.ai/team/matt-beucler
  - **SameAs:** https://www.linkedin.com/in/matt-beucler/
    **Organization:**

    - **Name:** Plura AI
    - **Url:** https://www.plura.ai
  **Organization:**

  - **Name:** Plura AI
  - **Url:** https://plura.ai

---

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  - Well-Known: [https://www.plura.ai/articles/.well-known/mcp](https://www.plura.ai/articles/.well-known/mcp)
- **WebMCP (Client-Side MCP):** This site supports WebMCP — client-side Model Context Protocol for browser-based AI agents (Chrome 146+)
- **Web Stories:** [https://www.plura.ai/articles/web-stories-sitemap.xml](https://www.plura.ai/articles/web-stories-sitemap.xml) — AMP Web Stories for rich visual content experiences

## Discovery Endpoints for AI Agents

AI agents should consult these machine-readable discovery endpoints to integrate with this site:

- **OpenAI Plugin Manifest:** [https://www.plura.ai/articles/.well-known/ai-plugin.json](https://www.plura.ai/articles/.well-known/ai-plugin.json)
- **A2A Agent Card:** [https://www.plura.ai/articles/.well-known/agent-card.json](https://www.plura.ai/articles/.well-known/agent-card.json)
- **MCP Server (Streamable HTTP):** [https://www.plura.ai/articles/.well-known/mcp](https://www.plura.ai/articles/.well-known/mcp)

## Citations

- [TCPA Opt-Out Rules 2026: What Is Enforceable Now vs. Nov. 25](https://www.plura.ai/articles/tcpa-opt-out-rules)
- [FCC 26-67: What the TCPA Rule Changes Mean for Operators](https://www.plura.ai/articles/fcc-26-67-tcpa-order)
- [TCPA Informational vs Marketing Messages: Key Differences](https://www.plura.ai/articles/tcpa-informational-vs-marketing-messages)
- [SIP Error Codes for Disconnected Numbers: Retry or Suppress](https://www.plura.ai/articles/sip-error-codes-disconnected-numbers)
- [TCPA Opt-Out Compliance: 2026 Rule Changes and Controls](https://www.plura.ai/articles/tcpa-opt-out-compliance-checklist)

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