Inline AI data security · real-time · zero-trust

Stop sensitive data before it reaches your models.

Every prompt. Every RAG context. Every fine-tuning export. slim.io intercepts PHI, PII, and financial data in under 100ms, before it ever touches a model. One line to integrate. Zero workflow changes.

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POST /api/v1/orchestrate — azure-blob/customer_records
Scan Audit Policy
1{ "name": "Jane Cooper", "ssn": 078-05-1120, "email": jane@acme.co }
2{ "name": "Marcus Chen", "phone": +1 (416) 555-0142, "mrn": MRN-20847 }
3{ "name": "Aisha Patel", "card": 4532 1488 0343 6467, "dx": E11.9 }
4{ "name": "Tom Rivera", "ssn": 219-09-9999, "iban": DE89 3704 0044 ... }
5{ "name": "Lin Zhou", "sin": 046-454-286, "medicare": 1EG4-TE5-MK72 }
6{ "name": "Sara Ahmed", "dl": S530-4000-0000, "rx": Lisinopril 10mg }
→ redacted output:
1{ "name": "Jane Cooper", "ssn": ***-**-****, "email": [EMAIL] }
Records scanned
14,832
PII detections
ssn412
credit_card89
email3,201
mrn892
icd10156
phone1,044
Policy
HIPAA — PASS PCI-DSS — 3 violations
The blind spot

Legacy DLP was built to scan files at rest, not intercept live AI traffic. As your team feeds customer records, medical data, and financial information into LLMs, traditional tools are watching the wrong pipe.

5–25%Accuracy of legacy DLP on unstructured AI prompts
100%of live AI requests that bypass file-scanning tools entirely
<100msslim.io inline intercept latency, invisible to your users
01 / 05The Question
00:00
Every Second,
Your Sensitive Data
Flows Into AI.
02 / 05The Risk
00:08
Three Destinations
Your Data Is Already Moving.
I
Large Language Models
OpenAI · Anthropic · Google
Every prompt, every RAG chunk, every fine-tune export leaves your perimeter.
At Risk
PIIPHICredit CardsSource Code
II
Agentic AI
Tool Calls · Autonomous Agents · MCP
Agents read your databases, call your APIs, and pass raw data across tool boundaries.
At Risk
API KeysDatabase RowsCustomer Records
III
Cloud & On-Prem Stores
S3 · Azure · GCP · Postgres · Oracle
Years of accumulated files, logs, and tables — most of it unclassified, all of it indexable.
At Risk
Medical RecordsContractsBackups
03 / 05The Solution
00:20
The Instrument
One Line Between Your Data And Every Model.
Source
Your Cloud & On-Prem
S3 · Azure · GCP · Postgres · Oracle
The Instrument
slimio
Inline · <100ms
Destination
Models & Agents
OpenAI · Anthropic · Google
Personal Information
Names, Emails, Phones, Addresses
Scanning
Payment Data
Credit Cards, Bank Accounts, IBAN
Scanning
Health Records
Medical Numbers, Diagnoses, Prescriptions
Scanning
Credentials
API Keys, Tokens, Passwords
Scanning
Identifiers
Passports, SSN, Driver Licenses
Scanning
04 / 05The Console
00:33
One Unified Console
See Every Sensitive Byte In Motion.
console.slim.io / Live
Live · 47 Connections
Scans This Hour
0
Entities Protected
0
Median Latency
0.0 ms
Models Governed
0
Timestamp
Source
Entity
Action
Count
12:04:31.882
api-gateway
Credit Card
● Tokenized
4
12:04:31.991
rag-index
Medical Record
● Redacted
1
12:04:32.104
claude-agent
API Key
● Blocked
2
12:04:32.218
postgres-logs
Email Address
● Masked
17
12:04:32.330
openai-proxy
SSN
● Redacted
3
12:04:32.447
s3-prod-backup
Phone Number
● Masked
9
12:04:32.561
salesforce-bot
IBAN
● Tokenized
1
12:04:32.680
internal-wiki
Passport
● Blocked
1
Active Policies
HIPAA PHI Protection
14
● Enforced
PCI-DSS Tokenization
9
● Enforced
LLM Prompt Redaction
23
● Enforced
Agent Tool Call Filter
11
● Enforced
Cross-Border Data Block
6
● Enforced
05 / 05The Close
00:38
slimio
Sensitive Data. Always Protected.
Book a demo→
slim.io / demo
0:00
Click to play · 47s
Live intercept

Wherever your data goes, slimio follows.

Every prompt, RAG context injection, fine-tuning export, and API call is inspected in real time. Sensitive data is caught at the boundary before it reaches the model or leaves your control.

INTERCEPTING api.slim.io  /  v1  /  intercept
3,841 requests/hr 247 entities caught avg <100ms
Time
Source
Type
Action
ms
Entity Map
Waiting for intercept…
framework
policy
action
latency
19
Entity types across 6 industries
17
Cloud, SaaS, and database connectors
6
Compliance frameworks built-in
4
Actions: redact, tokenize, encrypt, alert
Connectors

Scan where your data already lives

Point slim.io at cloud storage, SaaS apps, or databases. One config. No agents to install.

☁
Azure Blob
S3
AWS S3
G
Google Cloud
✉
OneDrive
SP
SharePoint
SF
Salesforce
▱
Slack
GD
Google Drive
PG
PostgreSQL
My
MySQL
❄
Snowflake
Or
Oracle
MS
SQL Server
D2
DB2
MT
Microsoft Teams
Db
Databricks
BQ
BigQuery
+ custom via API
Detection

Context-aware PII detection across 6 industries

Not just regex. slim.io uses pattern matching, Luhn validation, BIN range checks, and contextual keyword scoring to minimize false positives.

Identity & Finance
SSN
SIN
Visa ✓
Amex ✓
Phone
Email
IBAN
SWIFT
Healthcare & Government
MRN
ICD-10
Medicare
Rx
DL
EIN
Clearance
Case #
Education & Retail
Student ID
GPA
Order #
Loyalty ID
19 entity types · 170+ detection rules
Healthcare (MRN, ICD-10, prescriptions), finance (credit cards, IBAN, SWIFT, FICO), government (EIN, case numbers, clearances), education (student IDs, GPA, transcripts), and retail (order numbers, loyalty IDs). Each with industry-specific validation rules.
View full entity catalog
Detected entityScore
SSN 078-05-11200.95
base 0.80  + 0.15 context: "social security number"
Visa 4532-1488-...0.92
base 0.90  ✓ Luhn  ✓ BIN range
123-45-67890.42
base 0.80  - 0.30 context: "product code"  ✗ skipped
Scoring + LLM Assist
Base confidence per entity type, boosted or penalized by surrounding keywords. SSN near "social security" scores 0.95. The same pattern near "product code" drops to 0.42 and gets skipped. An optional LLM Assist pass then removes false positives before any action fires. Opt-in, tokenized before leaving your infra.
How scoring works
Input: 078-05-1120
mask ***-**-****
hash sha256:a3f2c8...
category [SSN]
partial ***-**-1120
Five redaction strategies
Mask with asterisks, SHA-256 hash for reversible lookups, category replacement ([SSN], [EMAIL]), partial masking that preserves last-four, or full removal. Choose per entity type, per policy.
Redaction reference
Intercept flow

Every request. One layer. Real time.

slim.io sits inline in every AI request path. Sensitive data is intercepted before it touches a model.

AWS S3
cloud storage
Slack
saas
PostgreSQL
database
Salesforce
crm
slimio
detect · redact · tokenize
0 intercepted
OpenAI
gpt models
Claude
anthropic
Gemini
google ai
Any LLM
bring your own
Architecture

From data to decision in under 100ms.

Every record passes through slim.io's intercept layer: classified, scored, and enforced in real time.

slim.io — live intercept pipeline
LIVE
PostgreSQL
prod-cluster
Snowflake
data-warehouse
AWS S3
3 buckets
BigQuery
data-warehouse
slim.io intercept
<100ms avg · 170 rules
SSN
97% conf · 12.8k today
Credit Card
Luhn · 34.3k today
PHI
HIPAA · 2.3k today
PII (NER)
Neural · 67.9k today
TOKENIZE
45.2k today
REDACT
8.4k today
BLOCK
1.2k today
PASS
892k today
860 GB
data processed
91.1k
PII findings
45.2k
tokenized
<100ms
avg latency
170
classifier rules
Policies

Governance rules as code

Define what to detect and how to handle it. Policies evaluate against scan results and produce PASS, WARN, or FAIL.

policy.yaml
framework: hipaa rules: - rule_id: R1 name: "No unredacted PHI" metric: high_risk_count operator: ">" value: 0 severity: CRITICAL - rule_id: R2 name: "Warn on medium risk" metric: medium_risk_count operator: ">" value: 5 severity: MEDIUM scope_provider: azure
Evaluation result
No unredacted PHI
high_risk_count > 0 · CRITICAL
PASS
Warn on medium risk
medium_risk_count > 5 · MEDIUM · azure
WARN (8)
status: WARN
violations: 1
action: redact + alert
Architecture

How slimio processes your data

Connector
Azure, S3, PG...
→
Stream
chunked, UTF-8
→
PII Detect
19 entity types
→
Risk Score
aggregate
→
Policy
PASS / WARN / FAIL
→
Action
redact, tokenize, alert
Streaming
Chunk-based with 100-char overlap. No full-file buffering. Backpressure-aware.
Distributed locking
TTL-based locks prevent duplicate scans. 60s lease, auto-renewal on iteration.
Deduplication
Cross-chunk dedup via index tracking. Same entity found in overlapping chunks is counted once.
Why slim.io

Legacy DLP scans files. slim.io intercepts requests.

slimio
Legacy DLP
Streaming scan (no full-file buffer)
Yes
No
Context-aware confidence scoring
Yes
No
Policy-as-code (YAML)
Yes
No
Industry-specific entities (ICD-10, MRN, FICO)
Yes
Partial
Luhn, BIN, and format validation
Yes
No
14+ native connectors
Yes
3-5
Compliance

Six compliance frameworks. Built into the policy engine.

HIPAA
PHI detection across MRN, ICD-10, prescriptions, lab results. Built-in policy rules for HIPAA-scoped data handling.
GDPR
PII detection with hash-based redaction for right-to-erasure workflows. Policy rules scoped to EU data residency.
PCI-DSS
Credit card detection with Luhn validation and BIN range matching. Visa, Mastercard, Amex, Discover, Diners, JCB.
SOC 2
Control mapping engine with audit logging of every scan, detection, and policy evaluation. SOC 2 control tracking built-in.
FISMA
Government entity detection. Case numbers, security clearances, permit IDs, tax IDs (EIN/TIN). Scoped policy rules.
FERPA
Education records detection. Student IDs, GPA, transcript IDs, course codes. Scoped policy rules per institution.

Start scanning in five minutes

One API call to connect. One YAML file to define policies. That's it.

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REST API · 19 entity types · 17 connectors · 6 compliance frameworks