Before you approve a vendor based on AI, prove the recommendation can survive challenge.

CyberShield shows what the AI claimed, what evidence supports it, what is missing or contradictory, what could happen if it is wrong, who must review it, and the strongest defensible next action.

Challenge One AI Recommendation See the 3-Minute Vendor-Risk Example

No integration required for the initial review. No autonomous approval. Your organization retains the decision.

How it works

1. Challenge the recommendation

Start with the AI recommendation your team is considering.

2. Separate the claims

CyberShield breaks polished AI output into reviewable statements.

3. Inspect the evidence

Missing, stale, self-reported, scope-limited, and contradictory support becomes visible.

4. Classify consequence

Risk If Wrong shows what could happen if the organization acts too quickly.

5. Preserve human authority

Accountable reviewers remain responsible for approval, rejection, deferment, or override.

6. Create the record

The result is a defensible AI Trust Decision Record with limitations.

Built for teams that must defend AI-influenced decisions

Vendor-risk teamsGRC and complianceCISOs and vCISOsSecurity reviewersAI governance leadersAccountable executives

Use CyberShield before relying on AI to

Approve a vendor.
Accept a security risk.
Submit a compliance response.
Rely on an AI-generated executive summary.
Act on an AI-generated recommendation.
Send a customer-facing answer based on AI output.

Is this another AI judging the first AI?

No. CyberShield does not treat a second model's opinion as proof. It separates the recommendation into claims, maps those claims to evidence, identifies missing and contradictory support, applies defined checks, classifies Risk If Wrong, and preserves the accountable human decision.

The controlled vendor-risk example

An AI recommendation says a vendor appears low risk and should be approved. For this evidence set, CyberShield shows why SOC 2 and encryption claims are not enough, why Risk If Wrong is High, why human review is required, and why Request Evidence is the strongest defensible action.

Controlled demonstration using synthetic vendor evidence. CyberShield does not approve vendors or replace accountable human review.

Designed by Dr. Max Justice

CISSP, Ph.D., vCISO, and cybersecurity leader with more than 25 years of experience across high-impact federal, healthcare, cloud, and enterprise environments.

Ready to examine more than one recommendation?

Explore a controlled 3-to-5 recommendation pilot with one defined decision domain, available evidence, and one accountable decision owner.

Explore the Controlled Pilot