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

I like Matters.AI - AI Security Engineer for Data's ability to provide good visibility into sensitive data across our organization and help us understand where it's stored and how it's used. The data lineage feature stands out because it helps track data movement across different systems, making it easier to identify potential data exposures. I appreciate how it supports DPDP Act compliance by improving data security, discovery, and governance. The platform is quite evolved and worthy, and its initial setup was very easy and smooth, taking just 15 minutes. Review collected by and hosted on G2.com.

I think it would be really helpful if there were more integrations and more automated compliance reporting. Detailed reports could be more useful with these improvements. Review collected by and hosted on G2.com.

We build AI infrastructure. Our platform processes customer ML workloads, training datasets, and model artifacts. Knowing where sensitive data ends up across our environment is not optional for us. It is a business requirement.

Matters.AI gave us that answer fast. I check the dashboard weekly and the posture score tells me immediately if anything needs attention. The scans pick up new data stores on their own, classify what is in them, and flag anything sensitive. I do not need to remind anyone to audit anything. It just runs.

The classification is sharp. It correctly distinguishes between a model configuration file and a file that contains actual customer data. That distinction matters when you are processing thousands of files across cloud storage. The accuracy has been consistent since day one.

The interface is clean. My team did not need a walkthrough to start using it. Findings come with a clear priority and a recommended next step. I hand it off, it gets fixed.Support has been responsive. Setup was done in a day. No disruption to our production workloads. Review collected by and hosted on G2.com.

Nothing that impacts our work. The product does what it says. If anything, having a few more export options for the findings data would be a nice convenience. Review collected by and hosted on G2.com.

Andromeda uses its DSPM capabilities to analyze the different data stores used in our product. The data insights and overall UX have been neat and intuitive. Its AI-powered classification and discovery feel like a clear differentiator in the market. The tool helps us identify and classify data, and it also recommends remediation in a way that feels genuinely innovative. Overall, it works as a complementary solution alongside our other security tools and practices. Review collected by and hosted on G2.com.

As a partner, I’d really appreciate having more co-branded marketing collateral and access to a dedicated partner portal. The product itself is strong, but the partner enablement side still feels like it could mature further to better support us. Review collected by and hosted on G2.com.

I handle project delivery for our security and data protection workstreams, and most tools in this space are built for full-time security engineers. They assume you live in the product eight hours a day. Matters.AI is different. It gives me what I need without demanding that I become a security specialist.

The first thing I noticed was the dashboard. I land on it and immediately see our posture score, open findings by severity, and what has changed since my last login. No clicking through five tabs to piece together the picture. The layout is intuitive enough that I walked two non-technical team members through it in under fifteen minutes and they were comfortable navigating independently.

The intelligence behind the scans is what keeps me confident in the output. The platform figured out which files contained personal identifiers, financial records, and access credentials across our cloud storage and connected applications. I did not have to set up detection rules or feed it sample data. It worked out of the box and the accuracy has been consistent enough that we stopped doing manual spot checks after the first few weeks.

Connecting our cloud environment and productivity tools took less than a day. I had budgeted a week for integration based on past experience with other platforms. That time went back into actual project work instead.

I specifically tracked system performance during the first month because our engineering team was cautious about anything touching live infrastructure. Zero impact. No latency, no resource spikes, no complaints from the engineering side.

From a cost perspective, we replaced a patchwork of manual processes and partial tooling with one platform. The return was clear within the first quarter, both in time saved and in the confidence we now have when answering questions about our data security practices.

The team behind the product has been available and responsive throughout. Setup was guided without being hand-holding. Post-deployment, they have flagged configuration opportunities before we asked, which tells me they actually pay attention to how customers are using the product. Review collected by and hosted on G2.com.

There is very little to flag here. If anything, the platform keeps getting better with each update, which means there is always something new to explore. Not really a complaint. Review collected by and hosted on G2.com.

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.

The biggest plus for me is how quickly it surfaces security issues. The interface is straightforward, and the AI insights make reviewing data security much less time-consuming. Review collected by and hosted on G2.com.

The main downside is that some of the AI recommendations can require manual review before taking action. I’d also like to see more integrations and customization options. 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.

It covers up most of the data security layer and wherever my data relies it can protect it by discovery and classification of it for complaint Review collected by and hosted on G2.com.

Still on the way to become more on azure Review collected by and hosted on G2.com.

Need to check the solution. As per the discussion about the product the dspm solution looks great Review collected by and hosted on G2.com.

As per the discussion, the is no log retention is there in matters DAM Review collected by and hosted on G2.com.