Nightfall DLP Platform

Ships policy templates for regulated data types such as PII, PHI and payment or financial data.

Provides an automated incident response workflow for data loss events.

Discovers and enforces data policies for content stored in or transiting through cloud applications and storage, extending DLP coverage to SaaS environments without endpoint agents.

Applies sensitivity labels to data automatically based on content analysis and context without requiring users to manually classify documents before policy enforcement.

Ships policy templates for nonregulated sensitive data types such as controlled unclassified information, intellectual property and source code.

Detects and controls sensitive data entered into generative AI tools, applying block, redact, or warn actions before data leaves the organization.

Correlates DLP policy violations with user behavioral context, distinguishing routine data movement from anomalous exfiltration patterns associated with insider threat or account compromise.

Applies preventative controls automatically such as blocking, encryption, alerting and user justification when sensitive data is detected.

Integrates with SIEM platforms for incident response.

Extracts text from images, scanned PDFs, and screenshots to classify and detect sensitive data that would bypass text-pattern matching.

Correlates user-centric content inspection across multiple channels to detect data loss.

Monitors and enforces data movement policies on endpoints, blocking or logging USB transfers, clipboard operations, print jobs, and screen captures of content matching classification policies.

Inspects prompts, uploads, and AI-generated responses for sensitive data across modalities, preventing exposure of regulated or proprietary information to third-party AI services.

Assesses and scores the risk of discovered AI services and embedded AI features (data handling, training-use terms, hosting, vendor posture) to drive sanction/block decisions.

Discovers and categorizes the organization's use of third-party AI, whether consumed as a service, installed locally, or embedded inside other applications, building a continuously updated inventory of AI usage including shadow AI.

Enforces AI usage controls through multiple local inspection points - browser, endpoint, and network - coordinated from a cloud-delivered control plane, so coverage does not depend on a single interception path.

Defines organizational AI usage policies and enforces them at the point of use - allowing, blocking, redirecting, or constraining specific AI services, models, and features per user, group, or data context.

Detects anomalous AI usage patterns - unusual volumes, off-policy services, atypical data flows to AI endpoints - and alerts on potential misuse or exfiltration through AI channels.