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ARMOR AI-DLP
Secure the front door. Email is where most attacks arrive — ARMOR AI-DLP controls sensitive data in real time at the AI interaction layer — block, redact or tokenise sensitive data in prompts before it reaches ChatGPT or Copilot, and control it in AI responses — so employees use AI without leaking data.
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Seclore ARMOR AI-DLP is data-loss prevention purpose-built for the AI era — it controls sensitive data in real time at the AI interaction layer, so employees can use AI (ChatGPT, Copilot, Gemini, custom AI apps and agents) productively without leaking sensitive data into AI models, and without sensitive data flowing back out through AI responses. It solves an urgent, new problem: as employees adopt AI tools, they paste and feed sensitive data into them — customer records, source code, financials, confidential documents, personal data — and that data can end up in the AI provider's systems, in training data, or exposed; meanwhile AI assistants connected to company data (like Copilot) can surface sensitive information to people who shouldn't see it. Traditional DLP wasn't built for this — it doesn't understand the AI interaction layer (the prompts going in, the responses coming out, the data AI systems can access). Seclore AI-DLP does: it inspects data at the AI interaction point and applies real-time controls — detecting sensitive data in prompts and blocking, redacting or tokenising it before it reaches the model (bi-directional tokenisation/masking), and controlling sensitive data in AI responses — so sensitive data is protected as it flows to and from AI, whether that's a public chatbot, an enterprise copilot, or a custom AI application. This lets organisations embrace AI's productivity benefits while preventing the sensitive-data leakage AI adoption otherwise causes. Seclore is an India-origin data-centric security pioneer (Mumbai-HQ), and AI-DLP (GA around 2026) is part of its 'Data Security Intelligence, built for AI' ARMOR platform. TechBag scopes, licenses and supports it in INR/GST for Indian enterprises.
The Seclore platform family
This page covers ARMOR AI-DLP — the AI-control layer. The rest of the Seclore ARMOR platform:
Quick facts
30-second orientation- Product
- ARMOR AI-DLP — DLP at the AI layer
- Vendor
- Seclore (Mumbai · IIT Bombay-incubated · ~2008)
- The category
- AI Data Loss Prevention (AI-DLP)
- The core
- Control sensitive data flowing to & from AI
- The problem
- Employees leak data into ChatGPT/Copilot; AI exposes data
- The mechanism
- Bi-directional tokenisation/masking at the AI point
- Covers
- Public chatbots, enterprise copilots, custom AI/agents
- The benefit
- Use AI productively without leaking data
- Platform
- The AI-control layer of Seclore ARMOR
- In India via
- TechBag — licensing, quotes, GST invoicing, support
Understand AI data loss prevention before you buy it
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is ARMOR AI-DLP?
DLP for the AI era — control sensitive data in real time at the AI interaction layer, so employees use AI without leaking data.
AI leaking data vs controlled AI usage — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | Unprotected / signature email | ARMOR AI-DLP (Seclore) |
|---|---|---|
| Data pasted into AI | Leaks, unseen | Tokenised/masked/blocked |
| Traditional DLP + AI | Blind to the AI layer | Controls prompts & responses |
| Copilot over-exposure | AI surfaces sensitive data | Response control |
| The AI dilemma | Ban (lose value) or allow (risk) | Enable safely |
| Shadow AI | Invisible leakage | Surfaced & controlled |
| Protection direction | One-way at best | Bi-directional |
| AI usage visibility | None | Seen & audited |
| DPDP + AI | Personal data leaks to AI | Controlled & evidenced |
AI-DLP enables safe AI adoption — control the data, not the tool. Bi-directional control addresses both leakage IN and Copilot over-exposure OUT. It's a new category; TechBag positions it. Seclore is India-origin.
The five pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Inspect at AI Layer
Inspect data at the AI interaction point — the prompts employees send to AI and the responses coming back — the layer traditional DLP can't see, where AI data leakage actually happens.
Detect Sensitive Data
Detect sensitive data in real time within AI interactions — customer records, source code, financials, personal data, confidential content — so you know when sensitive data is about to reach, or is coming back from, an AI model.
Control Inbound
Block, redact or tokenise sensitive data in prompts before it reaches the AI model — bi-directional tokenisation/masking — so sensitive data doesn't leak into AI providers' systems, training data or exposure.
Control Outbound
Control sensitive data in AI responses too — so AI assistants connected to company data (like Copilot) don't surface sensitive information to people who shouldn't see it. Protection in both directions.
Enable AI Safely
Let employees use AI tools productively — public chatbots, enterprise copilots, custom AI apps and agents — while preventing the sensitive-data leakage that AI adoption otherwise causes. Embrace AI, safely.
One agent on every machine, one console over all of them — modules attach without a second operational world.
Twelve capabilities. See, control, enable.
AI-DLP controls sensitive data flowing to and from AI — tokenised, masked or blocked, bi-directionally — the AI-control layer of the portfolio, and paired with the human firewall.
AI Interaction-Layer Inspection
Inspect data at the AI interaction point — prompts going in and responses coming out — the layer where AI data leakage happens and which traditional DLP can't see. Purpose-built for AI.
Real-Time Sensitive-Data Detection
Detect sensitive data in real time within AI interactions — personal data, customer records, source code, financials, confidential content — so you catch sensitive data as it flows to or from AI, not after the fact.
Public, Enterprise & Custom AI
Cover the AI employees actually use — public chatbots (ChatGPT, Gemini), enterprise copilots (Microsoft Copilot), and custom AI applications and agents — so data is protected across your whole AI usage, not one tool.
Bi-Directional Tokenisation & Masking
Tokenise or mask sensitive data before it reaches the AI model, and control it in responses — so the AI still works (on tokenised/masked data) but never receives or reveals the raw sensitive data. Protection both ways.
Block, Redact or Allow by Policy
Apply policy-based controls to AI interactions — block sensitive prompts, redact sensitive parts, or allow with conditions — so you enforce exactly what sensitive data can and can't flow to AI, per your rules.
Control Data in AI Responses
Control sensitive data in AI responses so AI assistants connected to company data (like Copilot) don't surface sensitive information to unauthorised people — addressing the AI over-exposure problem, not just prompt leakage.
Shadow-AI Awareness
Help surface and control unsanctioned AI use — the shadow AI tools employees adopt without approval — so sensitive data isn't leaking to AI you don't even know is being used.
Enable AI, Don't Just Block It
Let employees use AI productively rather than banning it — controls protect sensitive data while allowing the AI's value, so you embrace AI's benefits without the leakage, avoiding the false choice of ban-or-risk.
AI-Usage Visibility & Audit
See how AI is being used with your data — what sensitive data flows to which AI, what's blocked or tokenised — with an audit trail, giving governance and evidence over your organisation's AI data usage.
AI Compliance & Data Protection
Support compliance as AI adoption meets data-protection rules (India's DPDP, GDPR) — preventing personal and regulated data leaking into AI systems, and evidencing that AI data usage is controlled.
AI, Browser & Endpoint Integration
Integrate at the points AI is used — browsers, endpoints, enterprise AI platforms and custom AI apps — and connect with DSPM (what data AI can reach) and EDRM, so AI-DLP fits the ARMOR platform and your stack.
The AI-Control Layer of ARMOR
AI-DLP is the AI-control layer of the Seclore ARMOR platform — working with DSPM (discover what data AI can reach), Data Classification and EDRM (persistent protection) for end-to-end data security in the AI era.
Watch Seclore ARMOR in action
The overview, getting started, and protecting M365 email.
How Seclore ARMOR Works
The ARMOR data-security platform.
Where Seclore Belongs in Your Data-Security Framework
Seclore across the data-security stack.
Want a live, India-context walkthrough on your own fleet?
Book a guided demo →The endpoint catches what arrives. Email stops it arriving.
Here’s what genuinely sets Seclore ARMOR AI-DLP apart.
AI adoption is leaking your sensitive data — right now
The urgent problem Seclore AI-DLP addresses is that as your employees adopt AI tools, they are leaking sensitive data into them — constantly, often unknowingly — and traditional security isn't stopping it. Here's what's happening in every organisation: employees have discovered how useful AI is, and they're using it — pasting content into ChatGPT to summarise or rewrite it, feeding source code to AI to debug it, putting customer data into AI to analyse it, using AI copilots on company documents, and adopting all manner of AI tools for productivity. In doing so, they feed sensitive data into these AI systems: customer records, personal data, source code, financials, confidential documents, trade secrets. And that data can end up in the AI provider's systems, potentially in training data, logged, or otherwise exposed beyond your control — a genuine data-leak, often to a third-party AI service, sometimes across borders. This is happening at scale, right now, and mostly invisibly: employees don't think of pasting into ChatGPT as 'exfiltrating sensitive data', but that's what it can be. The well-known incidents of employees leaking confidential code or data into public AI tools are just the visible tip. Traditional DLP wasn't built for this: it inspects files, email, endpoints and network, but it doesn't understand the AI interaction layer — the prompts employees type into AI tools and the responses coming back — so it largely misses AI data leakage. Organisations face a bad choice: ban AI (losing its huge productivity benefits, and driving usage underground into shadow AI) or allow it (accepting uncontrolled sensitive-data leakage). Seclore AI-DLP resolves this by controlling sensitive data at the AI interaction layer specifically — so employees can use AI, but sensitive data doesn't leak into it. For any organisation whose employees use AI (which is now essentially all of them), this AI data-leakage risk is real, growing and largely unaddressed by existing tools — and AI-DLP is the answer. TechBag helps organisations control AI data leakage.
Control at the AI layer — what traditional DLP can't do
The core technical differentiator of Seclore AI-DLP is that it operates at the AI interaction layer — inspecting and controlling the prompts going into AI and the responses coming out — which is exactly where AI data leakage happens and exactly what traditional DLP can't see or control. Why traditional DLP falls short for AI: conventional DLP inspects data in files, email, on endpoints and across the network, applying policies to those channels. But AI interactions are different: an employee types or pastes content into an AI tool's interface (often in a browser), it goes to the AI model, and a response comes back. This prompt-and-response flow is a new channel that legacy DLP wasn't designed for — it doesn't understand the semantics of AI interactions, doesn't inspect prompts and responses as such, and often can't distinguish sensitive data heading into an AI model from ordinary web traffic. So AI becomes a DLP blind spot. Seclore AI-DLP is purpose-built for this layer: it inspects data at the AI interaction point, detects sensitive data in prompts and responses in real time, and applies controls precisely there — bi-directional tokenisation and masking (replacing sensitive data with tokens or masked values before it reaches the model, so the AI works but never sees the raw sensitive data), plus block, redact or allow-by-policy decisions. Critically, it works in both directions: it controls what sensitive data flows into AI (preventing leakage into models) AND what sensitive data comes out in responses (preventing AI from surfacing sensitive information to the wrong people). This AI-layer, bi-directional control is what makes it effective where traditional DLP isn't — it meets AI data leakage where it actually occurs. As AI usage explodes, having DLP that understands and controls the AI layer specifically is becoming essential, and it's a capability legacy DLP vendors are racing to add but that Seclore, as a data-security specialist, built deliberately. TechBag helps deploy AI-layer data controls.
Embrace AI's productivity — without banning it
A crucial value of Seclore AI-DLP is that it lets organisations embrace AI's productivity benefits rather than banning AI out of data-leakage fear — resolving the false choice between 'ban AI (and lose its value, and drive it underground)' and 'allow AI (and accept uncontrolled data leakage)'. The dilemma organisations face: AI tools deliver genuine, significant productivity — employees are more effective with them, and competitors are adopting them — so banning AI has a real cost and puts you behind. But allowing uncontrolled AI use means sensitive data leaks into AI systems, a real security and compliance risk. Many organisations, unsure how to control it, either ban AI (and watch usage go underground into unsanctioned shadow AI, which is worse — now it's uncontrolled AND invisible) or nervously allow it (accepting the leakage risk). Neither is good. Seclore AI-DLP offers the third, better path: control the data, not the tool. By protecting sensitive data at the AI interaction layer — tokenising/masking or blocking sensitive data in prompts, controlling it in responses — it lets employees use AI tools productively while preventing sensitive data from leaking into them. So you get AI's productivity benefits without its data-leakage risk: employees can use ChatGPT, Copilot and other AI to work faster, but customer data, source code, personal data and confidential content are automatically protected as they interact with AI. This 'enable, don't just block' approach is exactly what organisations need as AI becomes essential to how people work: you can't realistically ban it (and shouldn't, given the productivity), so you need to make it safe. Seclore AI-DLP makes AI use safe from a data perspective, so you can confidently adopt AI across the organisation. For businesses wanting to embrace AI while protecting their data, this is the enabling capability. TechBag helps organisations adopt AI safely.
Two-way protection — including the Copilot over-exposure problem
Seclore AI-DLP protects sensitive data in both directions — not just what employees put INTO AI, but what AI reveals in its responses — which is important because enterprise AI assistants like Microsoft Copilot create a specific, serious data-exposure problem that inbound-only controls don't address. The inbound problem (well-known): employees paste sensitive data into AI, and it leaks into the AI system. AI-DLP controls this by tokenising, masking or blocking sensitive data before it reaches the model. The outbound/response problem (less-discussed but serious): enterprise AI assistants connected to your company data — most prominently Microsoft Copilot, but also custom AI applications — can surface sensitive information to people who shouldn't see it. Here's why: these AI assistants access your organisation's data (documents, emails, files) to answer questions, and they inherit the existing access permissions — which, in most organisations, are over-broad (data over-shared, permissions never cleaned up). So when an employee asks Copilot a question, it may surface sensitive data from across the organisation that the employee technically has access to (because of over-permissioning) but shouldn't really see — salaries, confidential documents, sensitive records — that they'd never have found by manually browsing, but that AI helpfully retrieves and presents. This 'Copilot over-exposure' is a real, documented concern: AI makes over-permissioned data suddenly, easily accessible. Seclore AI-DLP's control of sensitive data in AI responses addresses this: it can prevent AI assistants from surfacing sensitive information inappropriately, controlling the outbound side. Combined with Seclore's DSPM (which finds the over-exposed data) and EDRM (which protects it), this tackles the AI over-exposure problem comprehensively. For organisations deploying Copilot or similar (which is many), controlling what AI reveals — not just what goes in — is essential, and Seclore's bi-directional approach addresses both. TechBag helps address AI over-exposure with Seclore.
Purpose-built for AI, from a data-security specialist and platform
Seclore AI-DLP is purpose-built for the AI era by a data-security specialist, and it's part of the broader ARMOR platform — which matters because securing AI data is genuinely a data-security problem, best solved by a data-centric specialist with a full platform, not a bolt-on. Data-security specialist heritage: Seclore has 15+ years of data-centric security expertise — protecting the data itself is its core competency — and AI data security is fundamentally about controlling sensitive data (as it flows to and from AI). So an AI-DLP from a data-security specialist, built deliberately for the AI layer as part of a 'Data Security Intelligence, built for AI' strategy, is well-founded — versus generic security tools adding AI features as an afterthought. Platform integration: AI-DLP is part of the ARMOR platform, working with the other layers: DSPM discovers what sensitive data exists and what AI systems can reach (so you know your AI data exposure); Data Classification labels sensitive data (informing what to protect); EDRM provides persistent protection of the data itself; and AI-DLP controls it at the AI interaction layer. Together, these address AI data security end-to-end: discover the AI data exposure (DSPM), and control it — both what reaches AI models and what AI reveals (AI-DLP), while protecting the underlying data (EDRM). This integrated approach is more effective than a standalone AI-DLP point tool, because AI data security spans discovery, control and protection. India origin: Seclore's Mumbai HQ and IIT-Bombay roots mean local presence, DPDP-awareness, and the option of an India-origin platform for AI data security — relevant as Indian organisations adopt AI under DPDP. For organisations securing their AI data usage, an AI-DLP built by a data-security specialist, integrated into a full data-security platform, from an India-origin pioneer, is a strong foundation. TechBag scopes AI-DLP within the broader ARMOR platform for comprehensive AI data security.
The honest scope
Seclore ARMOR AI-DLP is data-loss prevention purpose-built for the AI era — controlling sensitive data in real time at the AI interaction layer, bi-directionally (blocking/redacting/tokenising sensitive data in prompts before it reaches AI models, and controlling sensitive data in AI responses), across public chatbots, enterprise copilots and custom AI — so organisations can use AI productively without leaking sensitive data. From an India-origin data-security specialist, part of the ARMOR platform. The honest framing: AI-DLP / AI data security is a very new, fast-moving and crowded space, with many players — traditional DLP vendors adding AI controls, dedicated AI-security startups (protecting prompts and AI usage), CASB/SSE vendors adding AI controls, and cloud/AI platform vendors' own controls. It's an emerging category where approaches and capabilities are evolving rapidly, and no vendor is fully mature. Seclore's distinctive strengths are its data-security-specialist foundation (AI data security IS data security), its bi-directional control (both inbound leakage and outbound over-exposure like the Copilot problem), and its integration with the ARMOR platform (DSPM discovers AI data exposure, EDRM protects the data) plus India origin. Because it's a newer product in a new category, expect rapid evolution, and evaluate current capabilities against your specific AI usage. It's most compelling when you want AI data control from a data-security specialist as part of a full data-security strategy, when the Copilot/enterprise-AI over-exposure problem is a concern, and/or when you value an India-origin platform under DPDP. TechBag scopes Seclore AI-DLP honestly against the fast-moving AI-security field, positions it within your AI-adoption and data-security strategy, and licenses it in INR/GST with implementation support.
The numbers behind the platform
What your Seclore AI-DLP journey looks like
AI data-risk scoping
How your employees use AI (public chatbots, Copilot, custom AI), what sensitive data is at risk of leaking, and your DPDP drivers. TechBag scopes it free.
Deploy AI-layer control
Deploy AI-DLP at the points AI is used (browser, endpoint, enterprise AI), set policies (tokenise/mask/block), and start controlling sensitive data flowing to and from AI.
Both directions + Copilot
Tune inbound controls (leakage into AI) and outbound controls (AI over-exposure, incl. Copilot), and connect with DSPM (what AI can reach) and EDRM. Enable AI safely across teams.
Govern & scale
Use AI-usage visibility and audit for governance and DPDP evidence, extend across the organisation, and complete the ARMOR strategy. TechBag models it in INR/GST.
Trusted across regulated industries in 100+ countries
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“Our employees were pasting sensitive data into ChatGPT and we couldn't see it. AI-DLP tokenises it before it reaches the model — they still get the AI's help, we don't leak data.”
“The Copilot over-exposure control was the deciding factor — controlling what AI reveals, not just what goes in. Rolling out Copilot safely needed both directions.”
“We didn't want to ban AI — the productivity is real. AI-DLP let us enable it safely across the company instead of driving it into shadow AI.”
“That it comes from a data-security specialist matters — securing AI data IS a data problem. And the integration with DSPM (seeing what AI can reach) and EDRM made it a real strategy.”
“It's a new category — things are evolving fast — but bi-directional control and the platform fit made Seclore the right call for our AI data risk.”
“For DPDP, preventing personal data leaking into third-party AI was essential. AI-DLP gave us that control and the audit trail to prove it.”
“Being India-origin, with local support and DPDP-awareness, mattered as we adopted AI. And controlling data at the AI layer works. TechBag scoped it well.”
“We use it with DSPM and EDRM — discover what AI can reach, control it at the AI layer, protect the data. That end-to-end AI data security is the point.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and G2 retired its Grid. So here’s TechBag’s synthesis of the AI-security market — tap any vendor to see why it sits where it does.
TechBag Email-Security Grid
Execution strength vs product vision — the classic market map, minus the paywall.
AI-DLP from a data specialist, bi-directional, India-origin. This page's product.
Detection × Portfolio Integration
The grid nobody publishes — how strong the email detection is vs how integrated with the wider security portfolio.
Bi-directional + platform (DSPM/EDRM).
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
Seclore AI-DLP vs the AI-security field
Legacy DLP + AI add-ons, AI-security startups, CASB/SSE and Purview AI — honest lanes in a new category; the edge is bi-directional control from a data specialist, plus platform + India origin.
| Dimension | Seclore ARMOR AI-DLP | Legacy DLP + AI add-on | AI-security startups | CASB/SSE AI controls | Microsoft Purview AI | No AI-DLP |
|---|---|---|---|---|---|---|
| Position | AI-DLP from a data-security specialist; bi-directional; India-origin | Traditional DLP adding AI | Dedicated AI-security | SSE adding AI controls | Microsoft-native | The gap |
| AI interaction-layer control | Purpose-built at the AI layer | Bolted-on | Yes — the focus | Via proxy | In Microsoft AI | None |
| Bi-directional (in & out) | Both prompts & responses | Mostly inbound | Varies | Mostly inbound | Within MS | None |
| Copilot over-exposure | Response control + DSPM/EDRM | Not addressed | Some | Not the focus | MS access controls | None |
| Tokenise/mask before model | Bi-directional tokenisation | Redact/block | Varies | Redact | Some | None |
| Broad AI coverage | Public, enterprise, custom AI | Some | Broad | Web AI mainly | Microsoft AI | None |
| Part of a data-security platform | ARMOR (DSPM+EDRM+classification) | DLP suite | Standalone | SSE suite | Purview suite | None |
| India origin & DPDP fit | Mumbai-HQ, DPDP-aware, sovereign | Mostly foreign | Mostly US-origin | Mostly foreign | US-origin | N/A |
| Best fit | AI data control from a data specialist; bi-directional; India-origin | Extending existing DLP to AI | Dedicated AI-security-first | SSE-committed orgs | All-Microsoft AI estates | Nobody — AI leaks data |
Which email-security approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose Seclore AI-DLP if…
- ✓You want AI data control from a data-security specialist
- ✓Bi-directional protection (leakage in + over-exposure out) matters
- ✓You're rolling out Copilot / enterprise AI and worry about over-exposure
- ✓You value an India-origin platform under DPDP
Legacy DLP + AI if…
- ✓You want to extend your existing DLP with basic AI controls
AI-security startups if…
- ✓You want a dedicated, AI-security-first standalone tool
CASB/SSE AI controls if…
- ✓You're SSE-committed and want AI controls in that stack
No AI-DLP if…
- ✓Never — AI adoption leaks sensitive data without it
What do email threats cost you?
Drag the sliders (count AI users; IT-hour cost as loaded rate). Estimates assume productivity retained by enabling AI safely rather than banning it, plus reduced incident handling — but the far larger, unpriced win is the avoided breach and DPDP penalty (sensitive data leaking into third-party AI, and Copilot over-exposure, are real and growing). Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models actual device counts and modules.
Three ways to consume it
Seclore ARMOR AI-DLP is quote-priced (no public list) — by users, AI scope (public chatbots, copilots, custom AI) and whether you add the wider ARMOR platform (DSPM, EDRM, Classification) for end-to-end AI data security. It's a newer category — confirm current capabilities. TechBag right-sizes it and quotes in INR/GST — Seclore is India-origin.
ARMOR AI-DLP
Best for safe AI adoption
- Control sensitive data to & from AI, in real time
- Bi-directional: leakage in + over-exposure out
- Public chatbots, copilots, custom AI
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ The ARMOR platform
Best for end-to-end AI data security
- Add DSPM (what AI can reach), EDRM (protect), Classification
- Discover, control at AI layer, protect the data
- TechBag scopes the mix
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your device counts and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
Map how employees use AI — public chatbots, enterprise copilots, custom AI apps — to scope where control is needed.
Identify the sensitive data at risk of leaking into AI (customer data, code, personal data, confidential content).
If deploying Copilot/enterprise AI, consider the over-exposure risk (AI surfacing sensitive data) — needing outbound control.
Confirm the goal is to enable AI safely (not ban it) — controlling data while allowing productivity.
Plan for both inbound (leakage) and outbound (over-exposure) control — not just prompts.
Consider integration with DSPM (what AI can reach) and EDRM (protect the data) for end-to-end AI data security.
Map to DPDP — preventing personal/regulated data leaking into third-party AI, and evidencing control.
Given AI-DLP is emerging, evaluate current capabilities vs your AI usage; size and quote in INR/GST — TechBag scopes it.
Questions buyers ask
Ready to use AI without leaking data?
Scope AI data control (protect sensitive data flowing to and from AI, bi-directionally), enable AI safely instead of banning it, or let a TechBag advisor plan your AI data-security strategy.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.