From Manual Vetting to Continuous Trust: Automating Publisher Screening with AI

Why publisher screening is suddenly a software supply chain problem

A single compromised publisher can turn one bad update into a broad incident. In this context, a publisher is an organization or individual account that signs up to a distribution channel (for example, an app store, marketplace, package registry, or extension gallery) and ships software under their name—often by uploading binaries/packages and signing releases. When that update reaches thousands of machines, recovery isn’t just a patch—it’s investigation, containment, customer communication, and remediation at scale. In practice, that recovery almost always costs more than prevention—often far more—because the operational disruption, incident response labor, and downstream customer impact quickly outweigh the ongoing cost of stronger upfront screening.

That’s why publisher screening is a high-ROI investment: it reduces the chance that untrusted code ever earns distribution in the first place. Modern software ecosystems depend on third-party publishers—independent developers, tool vendors, and service providers—whose code runs in sensitive environments, so the blast radius can be systemic.

At Microsoft, Trust & Security Services runs automated publisher screening and continuous monitoring to help protect Microsoft-operated marketplaces and programs—as well as Microsoft more broadly—from fraudulent or compromised publishers.

Impact: Applying AI-driven publisher screening has reduced the total time spent on screening from several days to hours for the programs Trust & Security Services works with—improving reviewer throughput while still escalating the highest-risk or lowest-confidence cases for human judgment.

The challenge: manual reviews don’t scale (and static reputation checks miss change)

Publisher onboarding is often guarded by paperwork, one-time identity checks, and human review. That can work at small scale, but it breaks down with high submission volumes and fast-evolving attacker tactics. Reviews become slow and inconsistent, while “snapshot” reputation checks miss publishers whose risk changes over time.

The approach we employ in Trust & Security Services is an automated, AI-driven system that evaluates trust at two points: during onboarding and after approval as new evidence emerges. We use multiple specialized AI agents (a set of “checkers” looking at different evidence) and combine results into a calibrated risk score and recommended action—approve, deny, or escalate. Confirmed outcomes feed back into tuning so the system keeps pace with changing ecosystems and attacker behavior.

What the system produces (and why “explainable” matters)

For a high-volume platform, the useful output isn’t just a number. AI helps turn many partial signals into operational outputs you can act on:

• Decision recommendation: approve/deny/escalate.
• Calibrated risk score: a consistent measure so thresholds mean the same thing over time.
• Explanation bundle: top contributing factors, supporting evidence, and an audit trail.
• Why explainability matters: it speeds human review, enables appeals, and supports governance without “mystery model” behavior.

In practice, we mix evidence across identity corroboration, ecosystem reputation, and post-approval behavioral monitoring, informed by internal and external threat intelligence. We regularly validate and refresh signals against outcomes so the system stays effective as adversaries adapt.

Like other AI‑assisted decision support systems, this approach can make mistakes and its outputs depend on the signals and data available at evaluation time. Screening recommendations are designed to support (not replace) policy decisions, so we monitor error rates and reviewer overrides over time, and route low‑confidence or high‑impact cases for human judgment as part of the AI development and deployment life cycle.

Design considerations for real-world deployments

• False positives vs. throughput: overly aggressive thresholds drown reviewers and frustrate legitimate publishers. Many teams start by escalating uncertain cases, then tighten deny rules only when signals are strong and repeatable.
• Privacy and data minimization: collect only what you need for a defined purpose, bound retention, and apply strong access controls. Where possible, separate identity artifacts from behavioral analytics, and ensure reviewers can see what influenced a decision.
• Societal and statistical bias considerations: monitor for uneven error rates across relevant groups and contexts, use defensible risk-relevant features, and keep a human appeal path. AI can help improve consistency in how evidence is summarized and routed, but it can also introduce or amplify errors, so we measure outcomes and adjust thresholds, features, and reviewer guidance over time.
• Adversarial behavior: attackers adapt to published checks. Mixing independent signals and rotating features reduces single-point gaming.
• Continuous monitoring: trust can decay after onboarding. Re-score on meaningful events (for example, ownership changes or unusual submission bursts), not just on a fixed schedule.
• Human-in-the-loop: treat automation as triage. Give reviewers clear reasons and comparable past cases and make overrides easy to feed back into future screening. As the system matures and decision quality improves, the goal is that fewer cases require human input—reserving review capacity for genuinely ambiguous or high-impact decisions.

How it works: a practical walkthrough

At a high level, the flow runs from an initial publisher application to an automated decision, followed by ongoing re-evaluation as new evidence arrives.

Concrete example: A new publisher applied to join one of the programs that Trust & Security Services helps protect, claiming to be an established enterprise vendor. Automated checks found that the publisher’s “official” support domain was registered within the last month, the domain ownership/contact details didn’t align with the claimed company identity, and the signing EV certificate chain was newly issued with no prior distribution history on the platform. Individually, any one of these might be benign, but together they indicated elevated risk.

• Recommendation: Escalate (not an automatic deny) because identity corroboration is weak but not conclusively malicious.
• Reviewer prompt: request additional proof of organizational control (for example, verified domain control and business documentation) and confirm the relationship between the claimed company and the signing identity.
• Outcome handling: if verification succeeds, approve and continue monitoring; if verification fails or contradictions persist, deny and use the case to improve future screening.

High-level, the flow works as follows:

Figure 1: AI-Driven Publisher Screening End-to-End Flow

1. A publisher applies to distribute software through a platform, providing business and operational details (for example, ownership assertions, web presence, support commitments, and compliance attestations).
2. The onboarding request is forwarded to the AI solution for publisher screening.
3. A coordinated set of AI-assisted analyzers (agents) evaluates the publisher. Models combine signals into a recommendation (approve/deny/escalate) and a calibrated risk score that supports consistent triage.
4. The system correlates entities and signals (for example, matching ownership claims, domains, certificates, and accounts across sources), looks for inconsistencies and anomalous patterns, and generates a short reviewer-facing rationale highlighting the strongest contributing factors (without exposing the full detection recipe).
5. The platform enforces the decision, routing borderline cases to human review.
6. The decision is communicated back to the publisher.
7. After approval, the system monitors for change; new evidence can trigger re-evaluation, inform future decisions through outcome-based tuning, and prompt review of similar publishers.

What we’ve learned about operating publisher screening at scale

Two themes show up repeatedly. First, the best control is rarely a single “perfect” check—it’s complementary signals plus an escalation path that keeps review capacity focused. Second, trust changes over time, including when a legitimate publisher is compromised, so post-approval watchfulness is essential. Using confirmed outcomes to tune screening and policy turns vetting from a gate into an ongoing trust system.

Conclusion: trust is a lifecycle, not a checkbox

In our experience operating Microsoft marketplaces and programs, publisher vetting can’t be treated as a one-time manual control. At scale, the problem is best modeled as a lifecycle risk system: normalize heterogeneous signals into a calibrated score, map that score to policy thresholds (approve/deny/escalate), and attach an evidence-backed rationale suitable for audit and appeals.

After onboarding, trust must be maintained with event-driven re-scoring (for example, ownership changes, unusual submission cadence, or new threat intelligence) and enough telemetry to detect drift and respond quickly. This architecture pushes routine decisions into automation, reserves human review for low-confidence cases, and turns outcomes into feedback for ongoing tuning—while also delivering concrete operational gains (for example, reducing total screening time from several days to hours for the programs Trust & Security Services supports).