Gen Digital launches Agent Trust Hub to govern AI agents' data use
Gen Digital has introduced the Agent Trust Hub, a platform to manage how autonomous AI agents access, evaluate, and share data. The system adds a trust layer so agents can read trust signals directly, improving security and transparency across automated workflows.
For managers, this matters. AI agents move fast, and that speed can magnify data exposure, compliance gaps, and misinformation. A trust layer helps set guardrails before issues scale.
What the Agent Trust Hub is
Agent Trust Hub is a control point for data interactions between AI agents and your systems. It uses trust signals that agents can evaluate in real time, helping ensure sensitive information is handled with care and that actions are traceable.
Think of it as policy and proof stitched into your agent workflows: who can access what, under which conditions, with visible evidence.
Why managers should care
- Protects sensitive data as agents automate internal and customer-facing tasks.
- Reduces risk of agents spreading unverified or sensitive information at speed.
- Improves auditability for legal, risk, and security teams.
- Creates a single place to enforce policy instead of fragmented controls across tools.
How a trust layer typically works
- Signals: Provenance, data sensitivity labels, source reputation, and policy context.
- Policy checks: Rules that approve, deny, or request human review before an agent proceeds.
- Transparency: Logs and explanations of why an action was allowed or blocked.
- Containment: Guardrails that limit what data an agent can access and what it can output.
These patterns don't describe any single product feature set. They're the common building blocks teams use to keep AI agents safe and useful.
Practical next steps for leaders
- Inventory agent use cases and map them to data sources and sensitivity levels.
- Define trust signals you require (e.g., verified source, up-to-date consent, PII redaction).
- Set a decision flow: allow, block, or escalate to human review for edge cases.
- Run a pilot in a narrow workflow, measure results, then scale.
- Build an incident plan for misfires: rollback, revoke access, notify stakeholders.
- Close the loop: feed incidents and near-misses back into policy updates.
Governance and compliance alignment
Map your approach to widely used frameworks. They make audits easier and keep your team synchronized on risk language.
Metrics that matter
- Blocked data exposure attempts vs. false blocks.
- Time to approve or reject agent data access requests.
- Percentage of agent actions logged and explainable.
- Policy exceptions by business unit and use case.
- User satisfaction for teams relying on agent outputs.
Questions to ask vendors
- Which trust signals can agents evaluate natively? Can we add our own?
- How does it integrate with identity, data catalogs, and DLP systems?
- Where is the decision engine hosted? What are latency and uptime guarantees?
- What audit evidence is available for each decision? How long is it retained?
- What certifications and security practices are in place (e.g., SOC 2, ISO 27001)?
- How are agent misbehavior and model updates handled operationally?
Skills and training for your team
Upskill product, data, and risk owners on agent safety, policy design, and measurement. A shared baseline reduces friction between speed and control.
Bottom line
AI agents can accelerate work, but unmanaged access puts data and brand trust at risk. A clear trust layer-like the one introduced with Agent Trust Hub-helps you set policies once, apply them everywhere, and prove control.
Start with one workflow, measure outcomes, and expand with guardrails that your auditors, customers, and team can stand behind.
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