Operationalizing DSPM: 12 Must-Dos for Data & AI Security

Many organizations operate across hybrid, multicloud and SaaS environments, making it difficult to know where sensitive data resides, who has access and how it is being used. Without unified visibility, shadow data goes undetected, compliance gaps grow and the likelihood of breaches increases. DSPM provides a single pane of glass view, unifying visibility across all environments.

Toxic combinations occur when seemingly harmless risks overlap and create critical vulnerabilities. For example, a misconfigured cloud bucket that is publicly accessible may not seem catastrophic until it is found to also contain unencrypted PII being accessed by an AI pipeline. DSPM correlates such risks to surface the most dangerous exposures that traditional tools miss.

Modern enterprises cannot afford lengthy manual fixes for every alert. DSPM integrates with federated systems like ServiceNow, Slack or Jira to orchestrate remediation workflows. Policy driven automation resolves common issues instantly, while high-stakes risks are routed to the right teams for review. This hybrid approach reduces time-to-remediation without disrupting business agility.

Fragmented security tools often operate in silos, leaving gaps in visibility, governance and control. By consolidating data discovery, risk detection, compliance monitoring and remediation into one DSPM platform, organizations reduce complexity, improve collaboration across teams and ensure consistent enforcement of security policies.

AI relies on vast amounts of data but when security controls are missing, sensitive data can leak into AI pipelines or outputs. DSPM safeguards data by continuously monitoring usage, enforcing governance and ensuring compliance across AI workflows. This allows enterprises to unlock AI’s value while minimizing risks to privacy, trust and compliance.