Capabilities › Data Security
Data Security — you can't protect the data you don't know exists.
Sensitive data spreads faster than most inventories track it — copied into a SaaS tool, pasted into an AI prompt, replicated into a test environment. SIRI's data security capability starts with finding it, then controls access and flow around what actually matters.
27%Of employees have entered confidential company data into public AI tools
42%Of organisations had an exposed database reachable from the internet
60%Of security teams lack visibility into which AI tools are handling their data
Why data keeps spreading past where policy assumes it lives
Live tracking · scroll to see where sensitive data is actually ending up
AI data leakage
27%
27% of enterprise employees have entered confidential company data into public AI tools, including customer records and financial information (Salesforce, 2024).
Direct exposure
42%
42% of organisations had a database directly exposed and reachable from the internet, often without anyone realising it existed (Intruder 2026 ASM Index).
Visibility gap
60%
60% of security teams say they lack visibility into which AI tools are actually handling their data — meaning policy is being written for a picture that's already out of date.
Prompt-level exposure
11%
11% of data pasted into ChatGPT and similar tools contains sensitive or confidential information — a channel most data-loss-prevention tooling was never built to monitor.
Standardising
DSPM
Data Security Posture Management has emerged as the standard category for continuous, cloud-and-AI-aware sensitive-data discovery and protection.
Data governance policy usually describes where data used to live
By the time a data map is finished, a copy of that data is probably somewhere the map doesn't cover.
Most data security programmes start from an assumed inventory — the databases and systems of record everyone already knows about — and build access controls around that. The actual sensitive-data footprint is usually wider: copies in analytics tools, exports sitting in a shared drive, snippets pasted into an AI assistant, test environments seeded with production data. Controls built around the assumed inventory miss all of it.
The scale of the gap is visible in how sensitive data is actually moving today: 27% of employees have pasted confidential company data into a public AI tool, and 60% of security teams admit they lack visibility into which AI tools are even in use. Separately, 42% of organisations have a database directly exposed to the internet — meaning discovery has to cover both where data is supposed to be and where it's quietly ended up.
27% of employees have entered confidential data into a public AI tool
Against 60% of security teams who admit they lack visibility into which AI tools are handling their organisation's data — the gap between assumed and actual data flow is wide and growing. (Salesforce 2024; Cisco AI Readiness Index 2024.)
SIRI's data security capability starts with discovery — finding sensitive data across cloud, SaaS and AI/RAG pipelines, not just systems of record — then reviews access controls and data flow against actual sensitivity and exposure, feeding findings into SIRI Exposure for continuous tracking and SIRI AI Security for AI-specific data-flow risk.
What organisations get wrong
Four assumptions that leave real data flow unmapped
Most data security gaps aren't about missing policy — they're about a policy written for where data used to be.
01 — INVENTORY
“We know where our sensitive data lives”
Sensitive data spreads into analytics tools, exports, backups and AI prompts far faster than a manually maintained inventory tracks — the assumed picture is usually incomplete.
02 — AI CHANNEL
“Our DLP tooling covers this”
Most data-loss-prevention tooling wasn't built to monitor what gets pasted into an AI assistant — 27% of employees have already put confidential data through that exact gap.
03 — TEST ENVIRONMENTS
“Test and production are separate, so we're fine”
Test and development environments seeded with real production data carry the same sensitivity as production, but rarely the same access controls.
04 — CLASSIFICATION
“We have a data-classification policy”
A classification policy that hasn't been validated against actual data flow describes intent, not reality — discovery is what confirms whether the two match.
What SIRI's data security covers
Discovery first, then access control and flow, across every place data actually ends up
Cloud, SaaS and AI pipelines included by default, not treated as edge cases.
DISCOVERY
Sensitive Data Discovery
Finding sensitive data across cloud, SaaS, on-premises and AI pipelines, not just systems of record.
- Cloud & SaaS data discovery
- AI/RAG pipeline data mapping
- Shadow-data identification
ACCESS CONTROL
Data Access-Control Review
Reviewing who and what can access sensitive data, and whether that access is still justified.
- Access-permission review
- Over-permissioned data-store identification
- Data-access recertification
AI DATA FLOW
AI & RAG Data-Flow Risk
Assessing what sensitive data reaches AI models, prompts and retrieval pipelines.
- AI/RAG data-exposure mapping
- Shadow-AI data-flow assessment
- Prompt-level exposure review
CLASSIFICATION
Data Classification Validation
Validating classification policy against how data actually moves and where it actually lands.
- Classification-accuracy testing
- Policy-vs-reality gap analysis
- Ongoing classification drift tracking
EXPOSURE
Exposed Data-Store Assessment
Finding databases and data stores directly exposed to the internet or misconfigured for public access.
- Exposed database identification
- Storage-permission review
- Public-access misconfiguration
GOVERNANCE
Data Governance & Retention
Governance structures so data protection is maintained continuously, not assessed once.
- Retention-policy review
- Cross-border data-flow mapping
- Board-level data-risk reporting
Evidence, not guesswork
No data security programme vs. classification policy alone vs. SIRI Data Security — what actually differs
A policy document and a validated data-flow picture are different things.
| Approach | No data security programme | Classification policy alone | SIRI Data Security |
|---|---|---|---|
| Sensitive-data discovery cadence | None | Manual, periodic | Continuous |
| AI & RAG pipeline coverage | No | Rare | Included |
| Data-access control review | No | At policy level only | Validated against reality |
| Exposed data-store detection | No | Rare | Included |
| Classification validated against real flow | No | No | Standard |
Sources: Salesforce 2024; Cisco AI Readiness Index 2024; Intruder 2026 ASM Index. Summarised for comparison.
Numbers every board should know
Where sensitive data is actually ending up
27%
Pasted data into AI tools
Of employees have entered confidential company data into a public AI tool.
60%
Lack AI visibility
Of security teams say they can't see which AI tools are handling their data.
42%
Had an exposed database
Of organisations, reachable from the internet without authentication.
11%
Of AI prompts are sensitive
Of data pasted into AI tools contains sensitive or confidential information.
Compliance alignment
Standards & frameworks we align to
Our methodology is built around publicly recognised frameworks — not a proprietary checklist. Where a specific certification or attestation is completed and verified, it will be named here explicitly.
ISO/IEC 27001:2022
SOC 2 (AICPA TSC)
NIST CSF 2.0
MITRE ATT&CK
OWASP Top 10
OWASP Top 10 for LLM Applications
NIST AI RMF
ISO/IEC 42001
ISO 22301
CERT-In Directions 2022
Framework references reflect publicly available versions as of publication and describe the standards our methodology is aligned to; they are not a claim of certification, attestation, or audit completion unless stated explicitly elsewhere on this site.
Why SIRI for data security specifically
Discovery first — because you can't protect what you haven't found
Most data security programmes fail at the first step, not the last.
01
Discovery before policy
Sensitive data is found first, across cloud, SaaS and AI pipelines, before access controls are designed around it.
02
AI-aware by default
AI and RAG pipeline data flow is covered as a standard part of the assessment, not an afterthought.
03
Validated, not assumed
Classification and access-control policy is checked against how data actually moves, not taken at face value.
04
Connected to the rest of SIRI Security
Findings feed into SIRI Exposure for continuous tracking, Identity Security for access control, and SIRI AI Security for AI-specific risk.
Who this is built for
Organisations SIRI's data security is built for
Financial Services
Healthcare
Technology & SaaS
AI Companies
Organisations with cross-border data flows
Enterprises deploying AI on sensitive data
How we work
From discovery to continuous data-flow validation
01
Discover
Finding sensitive data across cloud, SaaS, on-premises and AI pipelines.
Weeks 1–202
Review Access
Assessing who and what can reach sensitive data, and whether that's justified.
Week 303
Validate Classification
Checking classification policy against actual data flow.
Week 404
Monitor Continuously
Ongoing discovery and exposure tracking as data flow changes.
OngoingFrequently asked
Data security, answered directly
How is this different from a data-loss-prevention (DLP) tool?
DLP tooling typically monitors defined channels against defined rules; this capability starts with discovering where sensitive data actually is — including AI and shadow-IT channels DLP often doesn't cover — before controls are designed.
Do you cover data flowing into AI tools specifically?
Yes — AI and RAG pipeline data-flow risk is a standard part of the capability, given how much sensitive data is now reaching AI tools outside traditional monitoring.
Can you tell us if our classification policy is actually accurate?
Yes — classification-accuracy testing validates policy against how data actually moves and where it actually lands, surfacing the gap between the two.
Do you find databases or storage that's exposed to the internet?
Yes — exposed data-store assessment is included, identifying databases and storage reachable from outside the organisation without authentication.
How does this connect to our existing access-management tooling?
Findings are designed to strengthen existing access-management and DLP investments by identifying what they're not currently covering, not to replace them.
Find your data before someone else does
Discover, then protect, what actually matters.
Start with discovery, or validate a classification policy you already have.
24/7 for active incidents: +91 79819 12046
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