Most AI support vendors will tell you they redact PII. Far fewer will let your data protection officer read the redaction policy, replay what was masked on a given ticket, and prove a Social Security number never reached a third-party model.
AI customer support with PII redaction and data masking is a category of agentic platforms that detect and remove or obscure personal data (names, card numbers, government IDs, health identifiers) before it is logged, displayed, or sent to a language model, while keeping enough context to resolve the ticket. In 2026, for regulated buyers in fintech, healthcare, and insurance, this has moved from a nice-to-have to a procurement gate.
PII handling now splits into four distinct capabilities: detection, redaction or masking, one-way hashing for tokens that must stay matchable, and data minimization that stops collecting what you do not need.
Conservative, GDPR-aligned detection (erring toward over-redaction on ambiguous strings) is the safer default for regulated workflows than aggressive minimization that risks leaking an edge case.
Contractual no-train agreements with model providers matter as much as redaction: even masked data should never be retained for training without an explicit agreement.
The evaluation question that separates real platforms from marketing pages: can you replay exactly what was redacted, masked, or hashed on any historical ticket and hand that to an auditor.
Data residency (US, UK, EU, AU) interacts with redaction: where masked and unmasked data live changes which regimes apply.
Last updated: June 2026
PII handling is where AI customer support gets genuinely hard for regulated companies. A support agent that resolves a card dispute has to read the card number to check the transaction, but that number should never land in a training set, a debug log, or a transcript a contractor can open. The naive approach (mask everything before the model sees it) breaks resolution, because the agent then cannot verify identity or look up an account. The reckless approach (send everything raw and trust the vendor) fails the first data protection review. The platforms worth shortlisting are the ones that thread this needle: detect personal data reliably, mask or hash what does not need to be in clear text, minimize what is collected at all, and leave an auditable record of every decision. This ranking evaluates seven vendors on exactly those criteria. It is buyer-neutral on the facts and honest about where each platform is strong and where it is not.
What Good PII Handling Looks Like in AI Customer Support
PII redaction and data masking in AI customer support is the set of controls that detect personal data in a conversation and then remove it, obscure it, or substitute it with a non-reversible token before that data is stored, displayed, or passed to a language model, without breaking the agent's ability to resolve the ticket. Mature handling treats this as four separate problems, not one.
Detection is the first problem and the one most vendors underinvest in. Pattern matching catches well-formed card numbers and emails. It misses a Social Security number a customer typed with spaces, a passport number in a free-text complaint, or a date of birth phrased as "I was born the day Kennedy was shot." Conservative, GDPR-aligned detection errs toward flagging ambiguous strings as personal data rather than letting them through, because in a regulated business a false negative is a breach and a false positive is an inconvenience.
Redaction and masking is what happens after detection. Redaction removes the data entirely (the log shows [REDACTED]). Masking obscures part of it while keeping a usable shape (showing only the last four digits of a card). The right choice depends on the downstream use: a transcript shown to a human reviewer can be fully redacted, while an agent verifying a partial card needs the masked form.
One-way hashing matters when a value has to stay matchable but should never be readable. If the agent needs to confirm that the email a caller gives matches the one on file, a one-way hash lets it compare without ever storing the address in clear text. Anonymization (irreversibly stripping identity) and pseudonymization (substituting a reversible token under controlled access) are the two ends of this spectrum, and a serious platform supports both.
Data minimization is the discipline of not collecting or retaining personal data you do not need. The cleanest PII is the data you never stored. Minimization means scoping what the agent reads, how long transcripts are retained, and which fields ever leave your environment.
Replayable redaction audit: A record showing, for any historical ticket, exactly which strings were detected as personal data, what action was taken (redact, mask, hash), and where masked and unmasked data were stored - the artifact a data protection officer or regulator uses during a review.
Lorikeet is an AI customer support platform built for complex, regulated companies, including fintechs, financial institutions, healthtechs, and insurers. Roughly 80% of its customers are US financial institutions and fintechs. It is built around conservative, GDPR-aligned PII detection and anonymization, contractual no-train agreements with model providers, role-based access control, and data residency in the US, UK, and AU - with every redaction and tool call captured in an audit trail compliance teams can replay.
At-a-Glance Comparison
At a glance
Platform: Lorikeet · Best For: Regulated fintechs and healthtechs that need conservative PII detection plus a replayable redaction audit · Data Handling: GDPR-aligned detection, anonymization, one-way hashing, contractual no-train, RBAC, US/UK/AU residency · Pricing: Per-resolution (~$0.80 chat/email/SMS, ~$1.00 voice; Coach ~$0.10/ticket)
Platform: Sierra · Best For: Enterprises wanting outcome-billed AI with standard enterprise security · Data Handling: Enterprise security posture and guardrails; redaction depth not publicly detailed · Pricing: Outcome-based, custom
Platform: Decagon · Best For: Large enterprises with embedded-engineering deployments · Data Handling: Enterprise security and access controls; redaction specifics quoted per deployment · Pricing: Custom, per-conversation or per-resolution
Platform: Fin by Intercom · Best For: Intercom helpdesk customers wanting drop-in AI · Data Handling: Inherits Intercom platform security and data controls · Pricing: $0.99 per resolution + helpdesk seat
Platform: Ada · Best For: Mid-market teams with high chat volume · Data Handling: Configurable sensitive-data handling within its automation layer · Pricing: Custom annual
Platform: Cognigy · Best For: Contact centers needing on-prem or private-cloud deployment · Data Handling: Deployment flexibility (on-prem/private cloud) supports data control requirements · Pricing: Custom enterprise
Platform: Salesforce Agentforce · Best For: Salesforce-native orgs · Data Handling: Salesforce trust layer with data masking and grounding controls · Pricing: Per-conversation, plus Salesforce licensing
The 7 Best AI Customer Support Platforms with PII Redaction and Data Masking in 2026
1. Lorikeet
Lorikeet is the AI customer support platform built for complex, regulated companies, and PII handling is treated as a first-class part of the product rather than a settings toggle. It resolves multi-step tickets end-to-end across chat, email, voice, SMS, and WhatsApp, and it is designed so a data protection officer can sign off on what gets detected, masked, hashed, and retained before launch. Detection is deliberately conservative and GDPR-aligned: it errs toward over-redaction on ambiguous strings, because in a regulated business a missed identifier is a breach.
Best For
Regulated fintechs, financial institutions, healthtechs, and insurers that need conservative PII detection, anonymization, and a redaction record their compliance team and regulators can replay. Around 80% of Lorikeet customers are US financial institutions and fintechs. Public proof points include a regulated fintech reaching roughly 85% automation with equal-or-better CSAT, handled on workflows where every redaction and tool call is logged.
Key Features
Conservative, GDPR-aligned PII detection that errs toward over-redaction on ambiguous strings, with anonymization rather than raw storage as the default.
One-way hashing for values that must stay matchable (confirming an email or phone matches on file) without ever storing them in clear text.
Contractual no-train agreements with OpenAI, Anthropic, and Gemini, so masked or unmasked data is never retained for model training.
Role-based access control and data residency in the US, UK, and AU, so where personal data lives is a deliberate choice, not a default.
Defence in depth around data handling: pre-launch adversarial simulations, inbound message checks, outbound guardrails, and 100% post-facto QA via Coach, with a replayable audit trail of every redaction and tool call.
Data Handling
GDPR-aligned conservative detection, anonymization, one-way hashing, RBAC, SOC 2, BAA-ready for HIPAA workflows, and US/UK/AU data residency. Lorikeet dynamically routes between Anthropic, OpenAI, and Gemini by task, all under contractual no-train agreements. Every redaction decision and tool call is captured for replay during a data protection review.
Limitation
Conservative, over-redaction-first detection is the right default for regulated workflows, but it can occasionally mask a string that was not actually sensitive, which means tuning during onboarding. Lorikeet is also purpose-built for complex regulated support; a small team wanting a quick FAQ deflection bot with no compliance requirements will find it more platform than they need.
Pricing
Per-resolution and outcome-aligned: roughly $0.80 per chat, email, or SMS resolution and roughly $1.00 per voice resolution, with Coach (standalone QA and analytics) around $0.10 per ticket. The customer defines what counts as a resolution and escalations are not charged. A representative Scale plan is 48,000 resolutions for $48,000 per year. For comparison, human-handled tickets typically run $1.25 to $4 each.
2. Sierra
Sierra is an enterprise AI agent company known for outcome-based pricing and a strong enterprise procurement story. It serves large brands across multiple industries and brings a standard enterprise security posture with guardrails on agent behavior. For PII specifically, Sierra publishes less public detail on detection, masking, and hashing mechanics than a regulated buyer typically wants to see before signing.
Best For
Large enterprises that want billing aligned to resolutions and a recognizable enterprise vendor, and that will validate redaction specifics directly with Sierra during security review.
Key Features
Outcome-based pricing where customers pay on resolution rather than per seat.
Voice, chat, and email channels under a branded agent approach.
Enterprise security posture and behavioral guardrails on the agent.
High-touch implementation with embedded Sierra staff.
Data Handling
Standard enterprise security and guardrails. The granular detail regulated buyers want on PII detection, masking versus hashing, and replayable redaction logs is generally surfaced during procurement rather than documented publicly. Confirm the specifics under NDA.
Limitation
The outcome-based pricing model can bias a vendor toward easier tickets, and the public PII documentation is thinner than a data protection officer in fintech or healthcare typically requires up front.
Pricing
Outcome-based, negotiated per customer. Rates per resolution are not published.
3. Decagon
Decagon is a high-end enterprise AI agent platform with large production deployments and embedded-engineering implementations. It carries an enterprise security and access-control posture and supports voice, chat, and email. As with most platforms at this tier, the specific PII detection, masking, and hashing behavior is scoped and confirmed per deployment rather than fully published.
Best For
Large enterprises with the budget and engineering capacity for an embedded, white-glove deployment, who want a premium AI vendor and will define data-handling specifics during onboarding.
Key Features
Per-conversation or per-resolution pricing, customer-selectable.
Voice, chat, and email in one platform.
White-glove deployment with embedded engineering during launch.
Enterprise access controls and security review support.
Data Handling
Enterprise security and access controls. Redaction and masking specifics are typically defined per deployment with the embedded team rather than documented as a public, self-serve policy. Ask for the redaction audit format before signing.
Limitation
The embedded-engineering model adds cost and a dependency on the vendor's team, and the data-handling configuration is less self-evident before procurement than a published, conservative-by-default approach.
Pricing
Custom, per-conversation or per-resolution, with annual contracts that run into six figures. Rates are not published.
4. Fin by Intercom
Fin by Intercom is the AI agent layered on top of Intercom's messenger and helpdesk, and it inherits Intercom's platform security and data controls. It is a fast drop-in for teams already on Intercom, with low published per-resolution pricing. PII handling is governed largely by Intercom's platform-level controls rather than a fintech-specific redaction engine.
Best For
High-volume teams already using Intercom (or comfortable adding it) that want a fast trial-to-deployment path and a low published per-outcome price, with standard platform data controls.
Key Features
$0.99 per resolved outcome, among the lowest published per-resolution rates.
Tight integration with the Intercom messenger and helpdesk.
Works with Salesforce and HubSpot helpdesks, not only Intercom.
Fast trial and deployment for existing Intercom customers.
Data Handling
Inherits Intercom's platform security and data-processing controls. Suited to general support data handling; teams with heavy regulated-PII requirements should validate detection depth and redaction logging against their data protection obligations.
Limitation
A low per-resolution price rewards volume on easy tickets, and PII controls are platform-level rather than purpose-built for regulated fintech or health data.
Pricing
$0.99 per outcome, plus an Intercom helpdesk seat fee if not already a customer.
5. Ada
Ada is an established AI automation vendor that has expanded from chat into voice and email, with a focus on autonomous resolution rate. It offers configurable handling of sensitive data within its automation layer and mature helpdesk integrations. Ada's strength is breadth and a long track record; regulated buyers should confirm how conservative its detection is and how its redaction is logged.
Best For
Mid-market and enterprise teams with high inbound chat volume that prefer an established vendor and have moderate-to-standard data-handling requirements.
Key Features
Multi-channel automation across chat, voice, and email.
Configurable handling of sensitive fields within automated flows.
Mature integrations with Salesforce, Zendesk, and major helpdesks.
Established deployment playbooks for large enterprises.
Data Handling
Sensitive-data handling is configurable within Ada's automation layer. Depth of detection and the format of any redaction audit should be validated against your specific regulatory regime during evaluation.
Limitation
Ada's roots are in chatbot automation, so the deepest regulated-grade controls (conservative detection by default, replayable redaction logs, one-way hashing) are less central to its positioning than for a purpose-built regulated platform.
Pricing
Custom annual contracts, not published publicly; commonly quoted in the tens to low hundreds of thousands per year by company size.
6. Cognigy
Cognigy is an enterprise conversational AI and contact center automation platform with strong deployment flexibility, including on-premise and private-cloud options. That flexibility is its main relevance to PII: organizations that need personal data to stay inside their own infrastructure can deploy accordingly. It is widely used in large contact centers across voice and chat.
Best For
Large contact centers and enterprises with strict data-control or residency requirements that favor on-premise or private-cloud deployment.
Key Features
On-premise and private-cloud deployment options for data control.
Voice and chat automation at contact-center scale.
Integrations with major telephony and CRM systems.
Enterprise governance and access controls.
Data Handling
Deployment flexibility supports keeping personal data inside controlled infrastructure. Specific PII detection, masking, and hashing behavior depends on configuration and the models wired in, so confirm the redaction and logging detail for your setup.
Limitation
On-premise control shifts more of the data-handling and redaction-tuning responsibility onto your team, and the platform is oriented toward contact-center automation rather than regulated end-to-end resolution by default.
Pricing
Custom enterprise pricing, not published.
7. Salesforce Agentforce
Salesforce Agentforce is Salesforce's agentic AI layer, built on its trust layer with data masking and grounding controls and native to the Salesforce data model. For organizations already standardized on Salesforce, it keeps customer data within that ecosystem and applies Salesforce's masking and access controls. Lorikeet coexists with Agentforce in some deployments where teams want a dedicated regulated-support agent alongside their Salesforce stack.
Best For
Salesforce-native organizations that want an agent operating inside the Salesforce trust layer and data model with minimal new data movement.
Key Features
Native to the Salesforce data model and CRM.
Salesforce trust layer with data masking and grounding controls.
Access controls and governance inherited from Salesforce.
Broad Salesforce ecosystem integration.
Data Handling
Data masking and grounding through the Salesforce trust layer, with personal data kept inside the Salesforce environment. The detection and masking behavior is governed by the trust-layer configuration; validate that it meets your specific regulatory obligations.
Limitation
The value is highest for organizations already committed to Salesforce; teams not on Salesforce inherit a heavier platform and licensing footprint to access the trust-layer controls.
Pricing
Per-conversation pricing on top of Salesforce licensing. Effective cost depends on existing Salesforce commitments.
PII handling is no longer a checkbox in regulated AI support procurement; it is the gate. See how Lorikeet handles conservative PII detection and replayable redaction.
How to Choose for PII Redaction and Data Masking
Generic CX buying guides start with deflection rate and CSAT. For a data protection officer those are downstream of one question: can you prove what happened to every piece of personal data. The lenses below separate platforms that survive a data protection review from those that pass on marketing claims.
Detection Conservatism
Ask how the platform handles ambiguous strings: a partial card number, a date of birth in free text, a government ID typed with spaces. Conservative, GDPR-aligned detection errs toward flagging these as personal data, because a missed identifier is a breach and an over-redaction is an inconvenience. Aggressive minimization that prioritizes keeping context can leak the edge case that ends up in a regulator's file.
Masking Versus Hashing Versus Redaction
These are different tools for different jobs. Redaction removes data from a log. Masking keeps a usable shape (last four digits) for verification. One-way hashing keeps a value matchable without ever storing it readable. A platform that only offers one of these is forcing a single answer onto problems that need three. Confirm all three are supported and configurable per field.
Replayable Redaction Audit
Detection and masking are only credible if you can prove them after the fact. The right standard is a record showing, for any historical ticket, which strings were detected, what action was taken, and where masked and unmasked data were stored. Ask whether you can replay the redaction decisions on a ticket from 90 days ago and hand that report to an auditor. Most vendors have logs; fewer have replayable redaction detail.
No-Train Agreements and Model Routing
Redaction is undone if masked data is retained for training. Ask which model providers the platform uses and whether there are contractual no-train agreements covering every one. A platform that routes between multiple model providers needs no-train coverage across all of them, not just the primary.
Data Residency and Minimization
Where masked and unmasked data physically live changes which regulatory regimes apply. Confirm available residency (US, UK, EU, AU) and how retention and field scoping are configured. The cleanest personal data is the data you never collected, so a platform that helps you minimize collection reduces risk before any redaction runs.
Questions to ask your vendor
Demos are built to look clean. The questions below are built to surface the gaps.
Show me the redaction record for a ticket from last week: which strings were detected, what action was taken, and where masked and unmasked data were stored.
What does your detection do with an ambiguous string that might be a government ID - flag it or let it through?
Do you support redaction, masking, and one-way hashing, and can I configure which applies per field?
Which model providers see customer data, and do you have contractual no-train agreements with every one of them?
Where can personal data be stored, and can I keep it in a specific region?
Can my data protection officer read and sign off on the redaction policy before go-live?
Lorikeet's Take on PII Redaction and Data Masking
Most AI support vendors will tell you they redact PII. The claim that matters is narrower: can your data protection officer read the policy, replay what was masked on any ticket, and confirm that no clear-text identifier ever reached a third-party model that could retain it. That is the bar regulated buyers should hold, and it is the bar Lorikeet is built to pass.
The deliberate choice is conservative detection. Lorikeet errs toward over-redaction on ambiguous strings, because in fintech, health, and insurance a missed identifier is a breach and a masked-but-not-sensitive string is a minor tuning task during onboarding. Combine that with anonymization by default, one-way hashing where values must stay matchable, contractual no-train agreements across the model providers it routes between, RBAC, US/UK/AU residency, and a replayable audit of every redaction, and the result is a system a compliance team can approve before launch rather than explain to a regulator after. See how it handles regulated support end-to-end.
Key Takeaways
PII handling in AI support is four problems, not one: detection, redaction or masking, one-way hashing, and data minimization. A platform strong on one and weak on the others is not safe for regulated workflows.
Conservative, GDPR-aligned detection that errs toward over-redaction is the right default for fintech, health, and insurance, where a false negative is a breach.
Contractual no-train agreements with every model provider a platform uses are as important as redaction itself; masked data should never be retained for training.
The decisive evaluation test is a replayable redaction audit: can you prove, ticket by ticket, what was detected, what was masked or hashed, and where data lived.
Lorikeet leads this list on conservative detection plus anonymization plus replayable redaction; Sierra, Decagon, Fin, Ada, Cognigy, and Agentforce are credible depending on existing stack, deployment model, and how much regulated-PII depth you need.
Conclusion
For regulated buyers in 2026, PII redaction and data masking have moved from a security questionnaire footnote to the deciding factor in AI support procurement. The platforms that pass are the ones that detect personal data conservatively, give you redaction, masking, and hashing as distinct tools, keep masked data out of any training set through contractual agreements, and let you prove all of it after the fact.
The seven platforms above each fit a different profile. Lorikeet is the answer for regulated fintechs, financial institutions, healthtechs, and insurers whose data protection officer is the toughest stakeholder in procurement and who need conservative detection, anonymization, and a replayable redaction record across chat, email, voice, and SMS. The other six are credible alternatives depending on your existing stack, deployment preferences, and how deep your PII requirements run.
If you are evaluating AI customer support against a data protection review, book a Lorikeet demo and bring your hardest data-handling requirements - we will walk you through detection, masking, and the redaction audit before you sign.
