Matters.AI - AI Security Engineer for Data Reviews & Product Details

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. Review collected by and hosted on G2.com.

The platform is evolving quickly with new features and capabilities being added regularly. It would be helpful to have a brief in-app changelog or "what's new" summary so that users can stay current with improvements without having to check separately. This is more of a wish-list item than an actual gap. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The customer-facing ROI reporting templates could be more polished for presenting business value to non-technical leadership. The underlying metrics and data are comprehensive, but having a few more executive-ready report formats would help security teams build the internal case for continued investment more efficiently. Review collected by and hosted on G2.com.

What I like most is that Matters.AI actually feels like it understands data instead of just yelling “sensitive data found” 500 times a day. As a security architect, that signal-to-noise ratio matters a lot, and here the AI-driven context around sensitivity, access patterns, and behavior makes alerts feel far more usable.

The AI Security Engineer concept is honestly what clicked for me. It feels less like another dashboard I have to babysit and more like a junior analyst who does not get tired, surfaces real issues, and even nudges toward remediation instead of just dumping findings.

UI and UX are refreshingly clean. I did not have to dig through five menus to figure out what is going on, which is rare in this space. Integrations are not massive yet, but enough to get started across key cloud and SaaS systems without too much friction.

Performance has been solid so far, and since it tries to bring DSPM, DLP-style visibility, and insider risk into one place, it cuts down the usual tool hopping, which is a quiet win for both efficiency and ROI.

On support and onboarding, it feels like working with a startup in a good way. Quick responses, open to feedback, and not stuck in rigid processes.

Overall, it feels like a modern data security tool built for how data actually behaves today, not just for ticking compliance boxes. Review collected by and hosted on G2.com.

From a security architect's lens, the biggest gaps are mostly around maturity, which is expected but still noticeable.

Some features feel like they are still evolving, especially when you compare them to more established DSPM or DAM tools. The vision is clear, but in a few places, you can see it is still catching up in terms of depth.

Integrations are another area that could improve. The basics are there, but in a typical enterprise setup, you end up wanting broader and deeper coverage, especially across edge cases and less common data sources.

There is also a bit of a balancing act between breadth and depth. It tries to cover DSPM, DLP-style controls, and insider risk in one platform, which is great, but naturally, some areas feel lighter than specialized tools.

On the UI side, while it is clean overall, I did run into moments where I wanted more advanced filtering or quicker drill-downs when investigating something specific.

You may also need some initial tuning to consistently get high signal alerts. It is definitely better than legacy noise-heavy tools, but not completely hands-off yet.

Support and onboarding are responsive, but still have that startup feel where documentation and structured guidance can improve over time.

None of these are deal breakers, just typical growing phase gaps. The foundation is solid, but it is still in that phase where you see both the potential and the rough edges. Review collected by and hosted on G2.com.

The AI Security Engineer from Matters.AI gives us something no other DSPM tool has delivered: actual clarity on where our sensitive data lives across cloud and SaaS. The automated classification is highly accurate, covering Indian PII patterns (Aadhaar, PAN, UPI IDs) out of the box. The risk-based exposure scoring lets me prioritize what matters instead of drowning in alerts. The agentic remediation capabilities reduce our mean time to respond to data exposure from days to hours. As a fintech CISO, the level of visibility this platform provides into data sprawl across AWS, GCP, and Google Workspace is exactly what we needed. Review collected by and hosted on G2.com.

The reporting dashboard could use a few more customization options for board-level presentations. Minor, but it would save time when preparing quarterly reviews. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

What we dislike about Matters.AI is that some workflows and configurations can take time to fully understand at the beginning, especially for first-time users. While the platform is powerful, a bit more onboarding guidance or simplification in certain areas could make the initial setup smoother. Review collected by and hosted on G2.com.