I manage both the DevOps function and project delivery at Dice, so I look at any new tool through two lenses: does it fit into our infrastructure workflows without creating toil, and can I predict its delivery timelines and outcomes reliably. Matters.AI scores well on both. On the DevOps side, the onboarding was clean. We connected our AWS account, the platform picked up our S3 buckets and RDS instances automatically, and scans started running without us having to write custom scripts or deploy sidecar agents. That matters when you are running a lean infrastructure team. The scans do not compete for compute resources on production, which was a concern I had going in. They run independently and the performance overhead is effectively zero. The classification results are where I stopped comparing this to other tools. We had previously tried building internal scripts to tag sensitive data across our storage layer. The coverage was maybe 60% on a good day, and maintaining those scripts was a project in itself. Matters.AI replaced all of that. The platform picks up user PII, payment identifiers, device data, and session tokens across both structured database tables and unstructured file stores. It does this without us feeding it custom rules for every data type. The accuracy has been high enough that our security team stopped running validation checks on the output after the first month. The second thing that stood out from a project management perspective is the guided remediation. Every finding comes with a risk score and a specific action to take. That means I can assign remediation tasks to team members directly from the findings view with clear priority and scope. No ambiguity, no back-and-forth on what needs to happen. For someone tracking sprint deliverables, that clarity is valuable. The exposure scoring dashboard has also become part of our monthly reporting cadence. Leadership gets a single number that reflects our data security posture, and they can see it trending over time. I did not have to build a custom dashboard or pull data into a BI tool. It was there out of the box.

What we like best about Matters.AI is how effectively it secures our data at fastn.ai by providing contextual, AI-driven insights with minimal noise. It helps us quickly identify real risks, automate responses, and maintain strong data visibility across our environment.

I work in cybersecurity advisory and spend a significant part of my time helping organizations evaluate and shortlist data security solutions. Having assessed several DSPM platforms in the market, Matters.AI is the one that consistently delivers during proof-of-value engagements. The difference becomes clear within the first scan cycle itself. Most DSPM tools I have seen rely heavily on static rule sets and regex-based classification. Matters.AI takes a fundamentally different approach with its AI Security Engineer model. The platform uses contextual AI to understand the nature of data rather than just matching patterns. This means the classification results are accurate from day one, with significantly fewer false positives compared to alternatives I have benchmarked it against. The breadth of the platform is another factor that sets it apart. DSPM, Database Activity Monitoring, and endpoint security are typically three separate line items in a security budget. Matters.AI brings all three together. For the organizations I work with, particularly in the Indian BFSI and payments space, this consolidation translates directly into faster deployment, lower total cost, and a single pane of glass for data risk. The engagement model deserves specific mention. The team does not operate like a typical vendor. They participate actively in solution scoping, deployment planning, and post-deployment optimization. Every interaction is technically productive rather than sales-driven, which is refreshing in the cybersecurity space. The outcomes I have observed across organizations using the platform have been uniformly strong. Teams consistently report that Matters.AI identified sensitive data and access risks that their existing tools had completely missed. That kind of first-scan impact is what turns an evaluation into a long-term adoption decision.