Secure ChatGPT and LLM use in real estate: protect tenant and buyer data in AI prompts

Shadow is for estate agents, brokers, property managers, landlords, mortgage brokers and proptech companies whose teams use large language models on listings, leases, applications and correspondence. It runs on your own computers or servers, replaces tenant, buyer, seller and landlord names, emails, phone numbers, addresses, bank details and recognised national IDs with realistic fakes before a request reaches the AI provider, and restores them in the answer. It reduces the personal data you share with AI vendors; it does not review AI decisions for fairness.

What data leaks into AI tools in real estate

Workflow	What typically ends up in the prompt
Writing listings and marketing copy	Property address, seller name, sometimes access codes and occupancy details
Tenant and buyer correspondence	Names, emails, phone numbers, viewing times, complaints
Lease drafting and review	Party names, addresses, rent, deposit, bank details, guarantor details
Rental applications and screening	Income, employer, references, national IDs, credit details
Mortgage and financing	SSNs, bank statements, account numbers, loan details
Maintenance and portfolio analysis	Tenant lists, unit addresses, arrears, contractor names

Rental and mortgage files are dense with personal and financial data, and agents often use free AI tools on their phones and laptops between viewings.

How Shadow protects it

Shadow masks values in every AI request and uploaded file with fakes from a persistent persona, records the mapping in an encrypted local vault, and restores the response.

Data	How Shadow handles it
Tenant, buyer, seller, landlord, guarantor names	Name detector, person: terms, NER sidecar or Presidio
Emails and phone numbers	Built-in detectors
Street addresses	English and German styles built in; NER sidecar or Presidio for other formats
Bank details and cards	IBAN (mod-97) and card (Luhn) detectors with valid-looking fakes
National IDs	US SSN and ITIN, UK National Insurance number, Indian Aadhaar and PAN, Australian TFN and ABN; more with Presidio
Agency, developer and contractor names	organization and org: terms
Leases, applications, rent rolls	DOCX and XLSX masked in place; PDF text masked
Tenant lists and arrears reports	report intent with typed tokens

organization = ["Harbour Lettings Ltd"]
terms = ["org:Initech Property Services", "term:Riverside Quarter"]
internal_domains = ["harbourlettings.example"]

[presidio]
enabled = true
url = "http://127.0.0.1:5002/analyze"
entities = ["PERSON", "LOCATION", "US_SSN", "US_BANK_NUMBER", "US_DRIVER_LICENSE", "UK_NINO"]

[policy.report]
person = "token"
email = "token"
phone = "token"
address = "token"
national_id = "token"
default = "surrogate"

[files]
pdf = "text"
images = "block"     # ID scans, payslips as photos and floor plans are not OCR'd

Known gaps for property data

• Addresses in other formats (outside English and German styles) need the NER sidecar or Presidio, and detection is best effort.
• Dates (dates of birth, move-in dates) are not masked.
• Account and loan numbers without a recognised format are not detected by the built-in detectors; there is no custom regex setting. Presidio’s US_BANK_NUMBER and the NER sidecar’s account-number detection help.
• Images and scanned documents are not masked. Many rental applications include ID photos and scanned payslips.

Regulations and frameworks

Requirement	How Shadow helps
GLBA Safeguards Rule (16 CFR 314), for mortgage brokers and lenders	A technical safeguard that keeps detected customer information out of AI vendor requests. It does not replace your written information security programme or service provider oversight
GDPR / UK GDPR Art. 5(1)(c), 25 and 32	Data minimisation and pseudonymisation for tenant and buyer data. Pseudonymised data is still personal data for you (Recital 26)
US state privacy laws (for example CCPA/CPRA)	Reduces personal information shared with AI service providers
Fair Housing Act and equivalent equality laws	Not addressed by masking. AI-written ads and AI-assisted screening still need human review for discriminatory content or outcomes
AML and KYC rules for estate agents	Masks identifiers in AI-assisted KYC work; it does not perform checks or keep records

Not legal advice

This page describes technical capabilities, not legal conclusions. Using Shadow does not make a business compliant with GLBA, GDPR, fair housing or AML rules. Work with your compliance adviser.

Example workflows

Drafting an arrears letter

What the property manager sends (all data fictional):

Draft a polite arrears reminder to Tom Barker, Flat 3, 221 Baker Street, London, tom.barker@mail.example, +44 20 7946 0018.
Two months owed; pay to IBAN GB33 BUKB 2020 1555 5555 55.

What the provider sees (your fakes will differ):

Draft a polite arrears reminder to Tom Barker, Flat 3, 221 Baker Street, London, tom.barker@mail.example, +44 20 7946 0018.
Two months owed; pay to IBAN GB71 WEST 1234 5698 7654 32.

Tom Barker is the recipient, so his name, address, email and phone pass through as written and each keep is logged; the model writes to the right tenant. The payment IBAN is not part of the recipient phrase, so the provider sees a fake that still passes mod-97, and the letter comes back with the real IBAN. Set [policy.act] targets = "surrogate" if tenant details must not reach the provider.

Reviewing a lease

An agent uploads a DOCX lease and asks for a plain-English summary of the break clause and the tenant’s obligations. Names of landlord, tenant and guarantor, addresses and bank details are replaced inside the Word document. The summary comes back with the real parties.

Portfolio arrears analysis

A finance manager uploads an XLSX rent roll of 450 units and asks “Analyse these records: which buildings have rising arrears?”. The request reads as a report, so the report intent turns tenant names, emails and phones into typed tokens; amounts stay as numbers. The analysis is restored locally.

Deployment patterns

Pattern	When to use it
Endpoint install on office laptops: shadow install --apply	Agents and property managers using API-backed chat clients and desktop apps
Gateway for a CRM or property management integration: http://127.0.0.1:8877/gw/openai/v1	Letter generation, enquiry triage and listing tools you build
Sidecar in Kubernetes	Proptech products calling LLMs for many customers. See Docker and Kubernetes

Shadow does not run on phones, so mobile AI apps are not covered; steer sensitive work to laptops with Shadow installed. There is no central admin console yet; see small businesses for a rollout checklist.

Related

• Redact documents before uploading to AI and privacy for AI in customer support
• Financial services and insurance
• File masking, detection, intent-aware policy
• Detection quality: benchmarks. Other approaches: compare

Frequently asked questions

Can property managers use ChatGPT on tenant data?

Not through the consumer chatgpt.com website with Shadow, because that is not a built-in intercepted host. With an API-backed client or an internal tool routed through Shadow, tenant names, emails, phone numbers, addresses, bank details and recognised IDs are replaced with fakes before the request leaves, and restored in the answer.

Does Shadow mask property addresses?

It detects street addresses written in English and German styles, and the optional NER sidecar or Presidio finds more locations. Addresses in other formats may not be detected. In listings the property address is often the point of the prompt, so decide per workflow whether it should be masked.

Can AI help with leases and contracts without exposing the parties?

Yes. DOCX leases are masked in place, so party names, contact details and bank details are replaced inside the Word file and the document stays valid. PDF text is extracted, masked and sent as text. Scanned leases are not OCR'd.

Does Shadow make AI tenant screening compliant with fair housing rules?

No. Masking names does not remove bias that can come from addresses, income sources, language or history, and Shadow does not evaluate model decisions. AI-assisted screening and advertising still need human review and legal guidance.

Does it help mortgage brokers with GLBA?

It supports the Safeguards Rule by keeping detected customer identifiers, such as names, SSNs, IBANs and (with Presidio or the NER sidecar) account numbers, out of requests to AI vendors. It does not replace an information security programme or vendor oversight.