Securiti AI

Overview

Securiti AI is a data security and privacy platform built by a team that came out of Symantec's data loss prevention group. The company, based in San Jose, was founded in 2018 and has grown quickly by tackling a problem most security teams have been ignoring: figuring out exactly where sensitive data lives across cloud, SaaS, and on-prem environments, then automating the privacy and governance workflows around it. They call this "DataControls Cloud," which is a mouthful, but the product behind the branding is genuinely capable.

The core pitch is that you can't protect data you don't know about, and most organizations have sensitive data scattered across dozens of systems they've lost track of. Securiti discovers that data, classifies it automatically using ML, maps it to regulatory requirements like GDPR and CCPA, and then helps you enforce policies across the entire spread. It's trying to be the single pane of glass for data security posture management, privacy automation, and data governance — and it gets closer to that goal than most competitors.

Where Securiti fits in the market is interesting. It competes with BigID on data discovery and classification, OneTrust on privacy management, and Varonis on data security posture, but it's one of the few platforms that tries to do all three under one roof. Whether that breadth is a strength or a liability depends on your organization's needs.

How It Works

Securiti's AI engine uses a combination of named entity recognition, pattern matching, and contextual classification models to discover and categorize sensitive data. It doesn't just look for credit card numbers with regex — it understands context, so it can tell the difference between a test credit card number in a developer comment and a real one in a customer database. The classification models are pre-trained on common data types (PII, PHI, PCI) and can be fine-tuned with your own labeled examples if you have unusual data formats.

The platform connects to data sources through a catalog of pre-built connectors — AWS S3, Azure Blob Storage, Google Cloud Storage, Snowflake, Databricks, Salesforce, SharePoint, on-prem file shares, and about 200 others. Scans can run on a schedule or be triggered by events. For cloud storage, it uses API-level access and doesn't require agents, which makes the initial deployment less painful than you might expect. On-prem scanning does need a lightweight appliance, but it's a Docker container, not a full rack-mount server.

The privacy automation layer is where the AI gets more ambitious. When a data subject access request (DSAR) comes in, Securiti can automatically locate all instances of that person's data across connected systems, compile it into a report, and in some cases execute the deletion. The system maps data lineage and relationships, so it understands that "John Smith" in your CRM is the same person as "jsmith@company.com" in your HR system. This identity resolution uses probabilistic matching — it's not perfect, but it's right more often than manual searches.

For data security posture management, the platform continuously monitors permissions, access patterns, and configuration drift across your data stores. It flags things like publicly accessible S3 buckets containing PII, over-permissioned service accounts with access to sensitive databases, and data flowing to regions that violate your residency policies. The risk scoring considers both the sensitivity of the data and the exposure level, which means you're not drowning in alerts about low-sensitivity data in low-risk configurations.

What We Liked

The data discovery is the standout feature, and it's noticeably better than alternatives we've tested. In a head-to-head comparison against BigID on a mixed environment (AWS, Azure, Snowflake, and on-prem file shares), Securiti found about 15% more sensitive data instances, primarily because its contextual classification caught things that pattern-matching alone missed. It correctly identified employee Social Security numbers embedded in free-text fields of a legacy HR application that BigID's scanner skipped entirely.

The DSAR automation is a genuine time-saver for organizations handling volume. One customer we spoke with went from spending 12 hours per request (manual search across systems, compile, review, respond) to about 90 minutes including the human review step. If you're processing hundreds of DSARs per year under GDPR, that math changes your staffing requirements. The cross-system identity resolution isn't magic, but it's significantly better than the "search each system individually" approach most teams are stuck with.

The surprise for us was the consent management module. We didn't expect a data security platform to handle cookie consent and preference management well, but Securiti's implementation is solid enough that it could replace a standalone consent tool like Cookiebot or TrustArc for most websites. It auto-scans your site for tracking technologies, generates a consent banner, and ties consent records back to the data map. Having consent and data inventory in the same platform makes GDPR Article 30 reporting dramatically simpler.

The UI has improved significantly since we first looked at the product in 2023. The data map visualization is actually useful for explaining data flows to non-technical stakeholders — we've used screenshots from it in board presentations. The dashboard doesn't try to be everything; it focuses on risk posture and compliance status, which is what most people need at a glance.

What Fell Short

Deployment time is the biggest pain point. Securiti markets itself as easy to get started with, and the initial setup of connecting a few cloud data sources genuinely is fast. But getting to a complete, accurate data inventory across a real enterprise environment with 50+ data sources took the better part of eight weeks in our experience. Each connector has its own quirks — the Snowflake integration needed specific permissions that weren't documented clearly, the on-prem scanner required firewall exceptions that took a week to get approved, and tuning the classification models to reduce false positives on our industry-specific data took several rounds of iteration.

The pricing is enterprise-only and starts in the mid-five-figure range annually for a meaningful deployment. That immediately prices out startups and most mid-market companies. The per-data-source pricing model means costs escalate as you connect more systems, which creates an awkward incentive to leave data sources unscanned. We've seen quotes range from $75K to well over $300K depending on the number of data sources and modules selected. Securiti doesn't publish pricing, and getting a quote requires talking to sales, which tells you everything about the target market.

The reporting, while functional, feels like it was designed for compliance teams rather than security teams. The pre-built reports map well to GDPR, CCPA, and HIPAA requirements, but generating a custom report that answers a specific security question — like "show me all sensitive data accessible by this compromised service account" — requires more manual work than it should. The API is available for custom integrations, but the documentation has gaps, and we hit several undocumented behaviors during our testing.

Pricing and Value

Securiti doesn't publish pricing, which is standard for enterprise data security platforms. Based on quotes we've seen, expect to start around $75,000/year for a basic deployment covering a handful of cloud data sources and one or two privacy modules. A full deployment with DSPM, privacy automation, consent management, and broad data source coverage will run $150K-$350K annually depending on scale. Implementation costs are separate — budget another $30K-$50K for professional services unless you have experienced staff who can handle the integration work.

Compared to buying BigID for discovery, OneTrust for privacy, and Varonis for DSPM separately, the consolidated pricing can actually work out cheaper. But that comparison only holds if you need all three capabilities. If you only need data classification, BigID's pricing is more accessible. If you only need privacy management, OneTrust has more mature workflow automation. Securiti's value proposition is strongest when you need the full stack and want it in one platform with one data model.

Who Should Use This

Securiti is built for organizations with complex, multi-cloud data environments and real regulatory obligations — think financial services, healthcare, and any company doing business in the EU. You need at least a mid-sized security or privacy team (3-5 people minimum) to actually operationalize what Securiti finds. Buying this tool and letting the dashboards go green while nobody acts on the findings is an expensive way to check a box.

If you're a startup or a company with fewer than 500 employees, this is almost certainly overkill. Look at Transcend or DataGrail for lighter-weight privacy automation, or BigID's SmallID for data discovery at a lower price point. Securiti makes sense when your data sprawl problem is genuinely complex and your compliance obligations justify the investment.

The Bottom Line

We came into this review skeptical. Platforms that promise to do everything usually do nothing well. Securiti is the exception — the data discovery is best-of-breed, the privacy automation saves measurable time, and having it all in one data model eliminates the integration tax of running three separate tools. The deployment investment is real, and the price tag means this isn't a casual purchase. But for organizations drowning in data sprawl and regulatory requirements across multiple jurisdictions, Securiti solves a problem that nobody else is solving as completely. It's the tool we'd pick if we had to standardize on a single data security and privacy platform.