Generative-AI adoption has exploded. Every prompt, embedding, and fine-tuning record now carries the same breach potential as a Social Security number or credit-card digit.
In response, data-security teams are discovering that their traditional playbooks—built for isolated databases and quarterly audits—simply don’t scale.
That’s where Data Security Posture Management (DSPM) comes in. DSPM platforms run continuous discovery, classification, and policy-enforcement across multi-cloud estates, flagging exposures the instant they appear.
In this guide, we rank seven leading vendors—placing Cyera first—for their ability to guard both classic regulated data and the new wave of AI artifacts.
The New Sensitive-Data Spectrum
Old-school categories, such as personally identifiable information (PII), protected health information (PHI), and payment-card data (PCI), are just table stakes.
Modern AI pipelines introduce fresh, often overlooked, risk objects:
- Prompt logs: chat transcripts that may capture customer secrets.
- Vector-database embeddings: numerical fingerprints that can be reverse-engineered back to source text.
- Model checkpoints: snapshots that can contain entire training datasets.
Ninety-five percent of organizations now store AI training data containing regulated PII. Forty-seven percent of employees admit to pasting company data into public AI tools.
Why DSPM Is the Linchpin for AI-Era Security?
Traditional DLP and CSPM tools look at endpoints or configurations. DSPM focuses on the data itself, answering three questions in real time:
- What data do we have?
- Where is it right now?
- Who can touch it?
Leading platforms:
- Continuously discover new stores the moment an engineer spins up a sandbox.
- Classify and risk-score every object by sensitivity, access context, and geography.
- Automate remediation—from encrypting a bucket to revoking an over-permissive role.
Seventy percent of organizations plan to add DSPM to their cloud stack within 12 months.
How We Ranked the Platforms?
- Coverage breadth: clouds, SaaS apps, data-lake formats, AI artifacts.
- AI-specific classification depth: out-of-the-box detection for prompt logs, embeddings, checkpoints.
- Deployment speed & developer friendliness: agentless scans, API/Terraform support, CI/CD hooks.
- Compliance & audit reporting: GDPR, HIPAA, PCI-DSS, FedRAMP.
- Pricing transparency and scalability.
The 7 DSPM Vendors Leading the Pack
1. Cyera
Cyera tops the list because it automatically discovers and classifies regulated data alongside emerging AI-related artifacts.
The platform auto-discovers assets across AWS, Azure, GCP, Snowflake, Databricks, BigQuery, and even on-prem object stores.
Cyera offers over 100 built-in data classifiers, including the ability to tag AI prompt logs.
A built-in identity engine correlates every dataset to human and machine identities, instantly answering: “Who can actually access this?”
Security teams can then push one-click remediations—encrypt, quarantine, revoke—to the exact account or role.
Cyera’s agentless architecture can complete an initial scan in under an hour, and developers can integrate Cyera via its documented REST API and Terraform provider.
Dashboards map data flows visually, helping execs prove compliance during GDPR or HIPAA audits.
For organizations racing to launch AI features without becoming tomorrow’s breach headline,
Cyera delivers the fastest path from visibility to action, which is why it is at the #1 spot.
2. Laminar
Laminar earns runner-up status for its granular data-lineage visualizations. Once connected to your cloud accounts, the platform traces every copy, move, and transformation, giving security engineers a movie reel view of how an S3 object becomes a Redshift table and then a BI dashboard.
That lineage context pairs with solid out-of-the-box classifiers for PII, PHI, and prompt logs. Laminar’s risk engine weights sensitivity, exposure path, and public sharing to generate an intuitive “critical, high, medium” score for each asset.
Mid-market companies appreciate the vendor’s consumption-based pricing, which starts lower than most enterprise rivals.
Downsides: native support for vector databases such as Pinecone or Weaviate is still on the roadmap, and remediation actions lean toward ticket creation rather than one-click fixes.
If your priority is seeing how sensitive data moves—not necessarily remediating it at lightspeed—Laminar is a compelling, budget-friendly choice.
3. Dig Security
Dig Security shines in real-time query inspection, making it a favorite among engineering teams that live in BigQuery and Snowflake.
The solution deploys an agentless sidecar that logs every data access request, detects anomalies (e.g., sudden 10 GB export by a service account), and can block or quarantine in flight.
Dig’s strength in GCP environments is unmatched—it ingests Cloud Audit Logs natively and surfaces misconfigured IAM roles alongside exposed datasets.
AI artifact coverage is growing; prompt-log detection is in public beta, while embedding-aware classifiers are expected by year’s end.
Deployment typically finishes within an hour, and Terraform modules make environment replication straightforward.
Weaknesses: AWS coverage is solid but less deep than GCP, and the UI can feel analyst-heavy for non-technical stakeholders.
Choose Dig if your data lake sits primarily in BigQuery and you need continuous monitoring down to the SQL-statement level.
4. BigID
Enterprise veterans will recognise BigID, which began in 2016 as a privacy-driven data-discovery tool and has since evolved into a full DSPM contender.
The platform boasts hundreds of pre-built classifiers spanning national ID numbers, health codes, financial records, and—via its new “BigAI” module—prompt logs, embeddings, and model artifacts.
BigID stands out for its privacy workflows: users can trigger DSAR fulfilment, do “right to be forgotten” deletions or run cross-border residency reports with a few clicks.
AI-artifact detection requires the BigAI add-on, which adds cost and an extra configuration step. Setup is heavier than newer rivals; on-prem connectors and role-based access take time to fine-tune.
Yet once operational, BigID’s policy engine is arguably the most feature-rich, supporting custom dictionaries,
Boolean logic and org-wide exceptions. Large, compliance-driven enterprises that need both privacy governance and AI-era visibility often find BigID worth the onboarding lift.
5. Open Raven
Open Raven approaches DSPM through the lens of visual threat-surface mapping. Its interactive graph shows every S3 bucket, RDS instance and Kafka topic, overlaid with sensitivity and public-exposure status.
That bird’s-eye view helps executives understand risk at a glance, while engineers can drill down to JSON-level object previews.
The product is Kubernetes-native, running as side-cars that ship findings to a SaaS console or your own Grafana if you prefer. It also supports on-prem object stores like MinIO—handy for hybrid deployments.
Open Raven excels at schema discovery, parsing Avro, Parquet and ORC files that other tools skim over.
Where it lags is automated remediation: Alerts can open Jira tickets or send Slack pings, but encryption or ACL fixes remain manual tasks.
AI-specific classifiers are limited to prompt logs for now. If you need crystal-clear visual maps—perhaps for board slides—Open Raven is hard to beat.
6. Varonis DSPM
Varonis built its reputation watching file systems and SaaS suites; the vendor’s new DSPM module extends that DNA to cloud object stores.
The key advantage is context convergence: a single dashboard shows who downloaded a SharePoint spreadsheet and whether that same user can query a sensitive Athena table.
That unified lens helps security ops teams spot cross-channel exfiltration attempts. Varonis offers pre-defined policies for GDPR, CCPA, and HIPAA, plus early-stage prompt-log detection.
However, coverage of vector databases and model checkpoints is limited to manual regex today, with fuller AI-artifact support slated for 2026.
Deployment uses lightweight collectors but still requires Windows servers for some telemetry, which may deter cloud-native startups.
For organizations already invested in Varonis for on-prem or Microsoft-365 security, the new DSPM add-on provides incremental value without learning a brand-new interface.
7. Sentra
Sentra rounds out the list with a SaaS-first, speed-to-value ethos. Sign-up happens in a browser, OAuth connects your cloud accounts, and the first discovery scan lands within 30 minutes—no agents, no Terraform.
The dashboard prioritises “Immediate Risks,” helping lean security teams tackle misconfigured buckets or exposed prompt logs fast.
Policy templates for GDPR, PCI, and CCPA come pre-enabled, and remediation actions can auto-apply encryption or private-link policies.
Platform breadth is the trade-off: At present, Sentra covers AWS, Azure Blob, and Google Cloud Storage but lacks native connectors for Snowflake, MongoDB Atlas, or vector databases. For many early-stage AI startups, that’s sufficient.
Thirty-eight percent faster mean-time-to-detect data exposure when DSPM feeds SIEM.
Implementation Tips: Getting Value in Your First Week
- Run a discovery scan across dev, test, and prod. The unknown-unknowns will surprise you.
- Tag AI datasets with business-owner metadata so incidents route to the right teams.
- Plug alerts into existing SOC workflows—Slack, PagerDuty, SIEM—so nothing falls through the cracks.
- Quick-win policy: block public buckets and repos that store prompt logs or model checkpoints.
Caveats & Counterpoints
DSPM isn’t magic. Expect false positives until classifiers are tuned to your data schema. Overlap with CSPM and legacy DLP can cause alert fatigue; rationalise tools to a “single source of data truth.”
Pricing often scales by scanned bytes, so forecast growth—vector databases balloon quickly.
Conclusion
Sensitive data now stretches from credit-card digits to ChatGPT prompt history. The vendors above—especially Cyera—help you discover, classify, and protect that entire spectrum without slowing AI innovation.
Implement DSPM today, and tomorrow’s breach headline may belong to someone else.
