How should security teams extend data discovery to audio and video files in cloud storage?

Why This Matters for Security Teams

Audio and video files are often where the most sensitive material hides: meeting recordings, customer calls, incident walkthroughs, screen captures, and training clips can all contain PII, credentials, PHI, or confidential business context. If discovery only covers documents and databases, teams leave a large blind spot in cloud storage. That matters because media files are easy to accumulate, hard to review manually, and frequently shared across collaboration and backup workflows.

Current guidance suggests treating media as data, not as a special case. The same policy logic used for text can be applied after transcription or speech-to-text extraction, then reinforced with storage controls from NIST SP 800-53 Rev 5 Security and Privacy Controls. NHI Management Group research also shows why this blind spot persists: 88.5% of organisations say their non-human IAM practices lag behind or merely match human IAM, which is a useful signal for the broader governance gap around cloud automation and content processing, as covered in the 2024 Non-Human Identity Security Report. In practice, many security teams discover exposed media only after a support ticket, legal hold, or incident response review has already forced the issue.

How It Works in Practice

The operational pattern is straightforward: ingest audio and video into the cloud, transcribe it with a controlled service, run the transcript through the same discovery and classification pipeline used for other sensitive content, and retain lineage so investigators can trace findings back to the original file. That means the security team is not trying to inspect raw media by eye at scale. It is instead extracting searchable text, metadata, speaker cues, timestamps, and where available, embedded subtitles or OCR from screen content.

To keep the process reliable, teams usually define three layers of controls. First, discover the storage locations where media lives, including object storage, shared drives, collaboration tools, and backup tiers. Second, apply policy to the extracted artifacts, not just the source file, so the transcript inherits the same handling rules as a document. Third, decide whether processing must stay inside the cloud boundary for regulatory or contractual reasons, which is often preferred when the content is highly sensitive.

• Use speech-to-text and media parsing to convert unstructured audio and video into reviewable text.
• Apply the same classifiers for secrets, PII, PHI, and confidential terms that you use for documents.
• Preserve file metadata, timestamps, and object links so remediation can reach the original asset.
• Restrict access to transcripts and derived data, since transcripts can be as sensitive as the source media.

For data governance structure, align this workflow with the NHI Lifecycle Management Guide and the broader risk patterns in the Ultimate Guide to NHIs — Key Challenges and Risks, because the same lifecycle discipline that governs secrets and access also applies to automated content pipelines. These controls tend to break down when media is stored in uncontrolled collaboration sprawl, because transcription jobs and derived transcripts are created faster than ownership and retention rules can be enforced.

Common Variations and Edge Cases

Tighter media discovery often increases processing cost, privacy review overhead, and false positives, so organisations need to balance coverage against operational friction. That tradeoff is especially visible in regions with stricter recording consent rules or in environments that mix enterprise data with customer-generated content. Best practice is evolving here, and there is no universal standard for every recording type.

One common edge case is encrypted client-side media where the cloud platform cannot inspect content until the file is decrypted in a trusted processing path. Another is multilingual content, where speech recognition quality affects classification accuracy and may require language detection before policy application. A third is screen recordings: the spoken track may be less sensitive than what appears visually, so teams should combine transcription with frame extraction or OCR when the risk profile justifies it.

Discovery also needs to account for inherited risk from adjacent workflows. Meeting platforms, call centers, and support tooling often auto-generate media files that are later synced into object storage, which means the real control point is sometimes the upstream system rather than the bucket itself. If the program cannot keep pace with file creation, search, and retention, the discovery engine becomes a reporting tool instead of a control. The practical limit appears when media volumes are high and retention ownership is unclear, because classification backlogs quickly erode the value of the whole workflow.

Related resources from NHI Mgmt Group

• How should security teams handle DWG files in cloud storage when they may contain export-controlled technical data?
• How should security teams reduce cloud data exposure from misconfigured storage?
• How should security teams evaluate data discovery tools for cloud, endpoint, and AI coverage?
• How should security teams govern access to cloud data beyond DSPM discovery?