MIND Earns Frost & Sullivan's 2026 Global New Product Innovation Recognition - What Product Teams Can Take From It
Frost & Sullivan recognized MIND with its 2026 Global New Product Innovation award for Data Loss Prevention (DLP). The firm highlighted MIND's strategy execution, measurable customer results, and speed in shipping AI-native controls for SaaS, hybrid work, GenAI, and agentic AI.
For product leaders, this is more than a trophy. It's a case study on how to turn customer pain into a focused roadmap, then scale it with a tight architecture that reduces noise and delivers outcomes.
Why this matters for product development
- Clear problem framing: MIND attacks alert fatigue, false positives, and blind spots in unstructured data-problems customers actually budget to solve.
- One AI layer, multiple decisions: A single classification layer powers discovery, detection, and prevention. Less duplication, fewer inconsistencies, faster iteration.
- Measurable impact: Customers report lower alert volumes, near-zero false positives in some deployments, and up to 80% less time spent running DLP programs.
- Strategy that travels: MIND performs against Frost & Sullivan's two lenses-strategy effectiveness and strategy execution-showing the roadmap isn't just smart, it's shippable.
What set MIND apart (and what you can emulate)
- AI-native from the core: MIND AI blends EDM, RegEx, NER, OCR, vector similarity, SLMs, and LLMs with proprietary statistical and predictive methods to classify both known and novel sensitive data types.
- Unified platform surface: DLP and Insider Risk Management (IRM) live in one system, so policy logic and context don't fragment across tools.
- GenAI-aware policy engine: Controls that understand prompts, approved tools, data types, and user behavior in AI workflows-designed for how people actually work.
- Endpoint + SaaS visibility: Local apps, AI agents, SaaS platforms, file shares, email-covered with lineage tracking and exfiltration prevention.
GenAI and agentic AI: Practical policy patterns
- Guardrails for prompts: Prevent sensitive data from being entered into prompts and capture intent-level context for reviews.
- Allow-list GenAI apps: Enforce access to approved enterprise GenAI tools; block shadow AI services.
- Workflow-aware detection: Identify sensitive data exposure across copy/paste, uploads, context windows, and agent handoffs.
- Agent visibility: Monitor AI agents inside SaaS and on endpoints, including data sources they touch and actions they automate.
Architecture choices that reduce false positives
- Layered classification: Combine deterministic (EDM, RegEx) with semantic approaches (NER, vectors, SLM/LLM) to raise precision without overfitting.
- Shared context: One model of data identity and sensitivity used across discovery, detection, and prevention to keep decisions consistent.
- Feedback loops: Use analyst actions and policy outcomes to auto-tune thresholds and patterns instead of constant manual tweaking.
Operational outcomes and the KPIs to track
- Precision/recall for sensitive data types across channels (SaaS, endpoints, email, file shares)
- Alert volume per 1,000 users and time to triage
- False-positive rate by source and policy
- Mean time to policy change and time-to-value after deployment
- Blocked vs. educated outcomes (prevention vs. coaching) and reoffense rate
Build vs. buy: A quick checklist
- Do you need semantic understanding of novel data types, not just patterns?
- Can you enforce across GenAI tools, agentic workflows, and endpoints without brittle workarounds?
- Will one classification layer feed discovery, detection, and prevention-or will you maintain three?
- How fast can you reduce alert fatigue without hiring a small army?
- What's the total cost to maintain policies, models, and integrations at scale?
Integration blueprint to ship sooner
- SaaS: CASB/SaaS APIs for file/object scanning, access control, and activity telemetry
- Endpoints: Native agent or EDR integration for clipboard, file system, device control, and local app/agent monitoring
- Email: Secure email gateways or API-based inspection for outbound protection
- Identity: SSO/IDP groups and attributes to tailor policies by role and data domain
- Data map: Connect to major storage and collaboration platforms to maintain lineage
- Telemetry: SIEM/SOAR for alert routing and automation
What Frost & Sullivan highlighted
The evaluation centered on strategy effectiveness and execution. MIND scored highly on both, with special mention of its GenAI-focused controls that reflect real customer usage, including visibility into GenAI tool activity, enforcement of approved apps, and prompt-level protections.
For context on the recognition program, see Frost & Sullivan's Best Practices Awards overview: Frost & Sullivan Best Practices Awards.
Quotes worth noting
"For GenAI-related risks, MIND introduced new controls after observing that many enterprises allow employees to use GenAI tools to process sensitive data... These additions demonstrate how closely MIND aligns its roadmap with customer pain points, further reinforcing its reputation for rapid capability development," said Daphne Dwiputriane, Research Analyst, Frost & Sullivan.
"The AI era is transforming how organizations create, move, and use data... our goal has always been to help security teams move beyond reactive controls and toward intelligent, automated protection," said Eran Barak, Co-Founder and CEO at MIND.
Fast facts about MIND
- Founded in 2023; headquartered in Seattle
- Unified platform for DLP and IRM with an AI-native classification core
- Coverage across SaaS, endpoints, on-prem file shares, and email
- Focus on lower alert volumes, fewer false positives, and faster time-to-value
Action for product teams
- Inventory where your current controls fail with GenAI prompts and agent workflows; treat those as first-class policy objects.
- Consolidate models and rules so detection and prevention share the same context.
- Design policies that prefer coaching on first offense and prevention on repeat behavior to cut support load.
- Instrument the KPIs above before rollout-baseline beats anecdotes.
Level up your roadmap skills
If you're building security products or guiding AI-native features, this learning path can help structure your approach: AI Learning Path for Product Managers.
Contacts
- Frost & Sullivan media: Ashley Shreve - ashley.weinkauf@frost.com
- MIND media: Michelle Kearney, Hi-Touch PR - 443-857-9468 - kearney@hi-touchpr.com
- MIND general inquiries: info@mind.io
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