Building evidence-led trust profile…

Evidence-led vendor trust, kept current.

NetRisk helps security and risk teams discover vendors, verify claims against available evidence, ask only for missing proof, quantify scoped risk, and revalidate trust as evidence changes.

Request paid pilot Join beta View sample trust profile

Beta and pilot access · Corporate-domain only · Decision-support software — validate critical findings before action.

Discovervendors from traffic, identity, spend & intake signals
Scopethe product, data access, owner & review depth
Verifyclaims against evidence before asking more
Revalidatefreshness, drift & material changes over time
Why third-party risk stalls

Questionnaires create answers. They rarely create trust.

Most programs start with partial inventories and long forms. Teams still have to prove which vendors exist, which claims are supported, which evidence is missing, and what risk remains.

Vendor inventory is incomplete

SaaS, AI tools, APIs, and subcontracted services appear in traffic, identity, spend, and procurement before the review queue catches up.

Attestations need evidence

A vendor saying "yes" is a claim. Teams still need source documents, dates, scope, contradictions, and a clear list of unanswered controls.

Risk is hard to explain

Without a scoped trust profile, exposure, evidence gaps, data access, and remediation tasks stay scattered across systems and spreadsheets.

Discovery Agent AI · detect & classify finds public & technical signals
Research Agent AI · read & extract reads docs, maps claims to controls
Reviewer human · approve accepts or rejects evidence
Monitoring Agent AI · watch & revalidate flags drift, expiry & new review signals
DNSchat.openai.com
14users seen
3departments
2SOC 2 / DPA
Rretention docs
EEntra SSO
CCASB / DLP
Bbrowser policy
⟳Scheduled re-check
AI
ChatGPT usage
Detected
An unknown AI tool appears.
It’s OpenAI · ChatGPT.
Who uses it, with what data?
Only missing proof is requested.
A human reviews the evidence.
Residual risk is scoped.
Trust is revalidated.
ChatGPT usage
OpenAI · employee AI assistant
Detected
—
Trust confidence
◉
OpenAI / ChatGPT
resolved from traffic
identified
◦
14 users · 3 depts
customer data possible
scoped
▤
DPA & security docs
requested only where missing
gaps
✓
Team plan, no personal use
reviewer approved · limited
reviewed
⇄
Access and data scope
risk scoped from evidence
scoped
⇄
Compensating actions
owner review required
scoped
⟳
Expiry, drift & new signals
queued for revalidation
recheck
Evidence review

A vendor answer isn't proof.
It becomes a claim to review.

NetRisk never trusts an answer directly. Every answer becomes a claim — linked to its source, checked against scope, routed to a reviewer, and only then does the trust state move.

Claim under review "Customer prompts are not used to train models." extracted by AI from OpenAI enterprise & privacy docs
01
Claim
Prompts not used for training
AI-extracted statement
02
Source
Enterprise privacy terms
linked & cited
03
Scope
Team/Enterprise only
not personal accounts
04
Reviewer
Conditionally reviewed
security + legal review
05
Trust state
Reviewed with conditions
profile updated
AI proposes the claim scope & conflicts surfaced reviewer approves the trust state A claim is only trusted when evidence, scope, and a reviewer agree.
Discover
Found in your real traffic
NetRisk discovered Stripe from network telemetry.
Node
Hover a node
reviewed / configured evidence observed data flow missing evidence exposure path
€72k
annualized loss
High until scoped
Possible customer and internal data shared through unmanaged ChatGPT usage — no DPA, no enforced SSO, no data policy.
14 users identified 3 missing controls sensitive data possible
Evidence basis
usage observed · controls missing
Confidence
Medium
Data at risk
Customer + IP
Risk level
High
Recommended treatment
Consider managed plan, SSO, allowed-data rules, personal-use restrictions, and revalidation triggers.
ChatGPT usage
OpenAI · employee AI assistant
Queued · revalidating
54
Trust status
Conditional
scopeChatGPT usage
users14 · 3 depts
Evidence
Security docsreviewed
DPA statusreviewed
Admin controlsmissing
AI usage exposure
Risk exposure
levelHigh until scoped
data at riskcustomer + IP
Confidence
overall54 / 100
missingadmin, policy, owner
Next action
Reviewer decisionPending
Enforce SSORequired
Define data policyAssigned
Verification operating model

Evidence-led workflows —
not blind trust in AI.

NetRisk follows one review loop: AI assists → evidence supports → humans approve → changes trigger revalidation. Evidence and reviewers decide the trust state.

discovery
Discovery Agent
AI proposes

Looks at
Produces
→
Supports decision:

AI accelerates the review. Evidence and human approval decide the trust state. NetRisk can assist discovery, research, extraction, and monitoring, but beta results remain decision-support and require validation before action.

Evidence maturity model

Start with evidence.
Escalate only the gaps.

NetRisk separates self-attestation, document support, observed technical signals, configured integrations, and revalidation triggers so reviewers can see how mature each trust claim really is.

Layer 1

Documents & questionnaires

self-attested & document-supported

2SOC 2 ISOISO 27001 DDPA SSubprocessors QQuestionnaire
Layer 2

Public & technical signals

inferred from the outside

DDNS / TLS HSecurity headers crtcrt.sh ShShodan CeCensys
Layer 3

Configured systems & logs

reviewed where a pilot connection exists

Identity OkOkta EEntra GGoogle WS Cloud aAWS AzAzure GGCP Code GiGitHub GlGitLab Posture WWiz SnSnyk TeTenable GRC VaVanta DrDrata
Layer 4

Continuous revalidation

freshness and change detection over time

SlSlack alerts TeTeams JJira SNServiceNow ⟳Freshness & drift

Integrations shown represent supported categories and pilot targets. Validation depends on configured connections, available evidence, and reviewer approval.

What makes it different

Less questionnaire theater.
More evidence-led trust.

Discover
vendors before the form

Use traffic, identity, spend, and intake signals to find vendors that may not be in the official inventory yet.

Verify
claims against proof

Map answers to documents, public signals, review states, and missing evidence instead of accepting self-attestation.

Quantify
scoped risk

Explain risk using evidence quality, data context, exposure, and unresolved gaps so the decision is traceable.

Revalidate
trust over time

Track expiry, new signals, and material changes so the trust profile does not silently decay after approval.

Sample profiles use illustrative demo values for clarity.

Buyer examples

Useful for every team
that has to trust a vendor.

NetRisk gives each buyer a shared evidence trail for beta and pilot reviews without replacing reviewer judgment.

Security

Need

Find vendors and AI tools before exposure becomes an incident path.

NetRisk

Connects discovery, evidence gaps, attack-surface signals, and risk scope.

Output

A reviewed trust profile with open risks and follow-up evidence requests.

GRC

Need

Show why a control claim was accepted, rejected, or still missing.

NetRisk

Links each claim to evidence, freshness, scope, reviewer state, and gaps.

Output

An audit-friendly rationale for beta and pilot vendor decisions.

Procurement

Need

Know what is blocking approval without translating security jargon.

NetRisk

Shows missing evidence, risk conditions, owners, and next actions.

Output

A clearer path to approve, conditionally approve, or pause a vendor.

Vendor trust

Need

Publish a credible picture of what is proven and what still needs review.

NetRisk

Creates scoped trust profiles that distinguish verified, missing, stale, and illustrative data.

Output

A sample-ready evidence model for trust-center and buyer conversations.

Revalidation · change feed

When trust changes,
the profile changes.

Configured signals can flag evidence expiry, new vendor activity, control drift, and follow-up tasks. During beta and paid pilots, these signals support reviewer decisions rather than replacing them.

New AI domain detected
discovery · claude.ai seen in traffic
inferred
DPA evidence expired
ChatGPT usage · freshness lapsed
stale
SSO control missing
Entra ID · not enforced for AI app
risk
Data policy reviewed
reviewer · sensitive-data rule proposed
reviewed
Claim moved: inferred → document-supported
no training on prompts · enterprise terms
upgraded
Configured control reviewed
CASB · DLP policy evidence attached
reviewed
New user started using ChatGPT
access-path change · review opened
risk
Trust profile queued for review
freshness changed · sample event
review
Revalidation signal

Continuous revalidation keeps vendor trust from going stale.

Instead of waiting for the next annual review, a trust profile can show expiry, drift, new signals, and fresh vendor evidence that should be reviewed.

When vendor trust changes, NetRisk helps teams see the reason, review the evidence, and decide the next action.

Sample trust profile

View a sample scoped vendor trust profile.

Walk an illustrative sample profile showing discovery source, reviewed evidence, open gaps, external exposure inputs, scoped risk, and provenance. It is demo data, not live vendor analysis.

View sample trust profile Analyze in workspace after signup
ChatGPT usage
Conditional
trust confidence54 / 100
docs reviewedcontrols missinghigh until scoped
Pricing / paid pilots

Beta access now. Design partner pilots when you need a scoped outcome.

Public pricing is for demand capture and pilot scoping only. No production billing, checkout, entitlement, or account-tier logic runs from netrisk.io.

PackageFree beta

Limited corporate-domain evaluation, sample trust profile access, and feedback-led extension.

Join beta
PackageDesign Partner Pilot

$10k-$20k for a 90-day scoped pilot with vendor cohort, evidence review, gap requests, profile previews, and risk summary.

Request paid pilot
PackageBusiness

Annual plan after a validated pilot, with expanded vendor review volume and evidence-led operating workflows scoped by agreement.

Talk to founder
PackageEnterprise

Custom annual scope for regulated teams with procurement, security, GRC, vendor trust, legal, and retention stakeholders.

Talk to founder

Lead capture asks only for company, role, work email, domain, vendor count range, pain point, and desired pilot timeline. Do not submit secrets, evidence files, or internal security data.

Get started

Build evidence-led vendor trust profiles.

Discover vendors, verify claims, quantify scoped risk, and keep trust current with evidence-first revalidation.

Request paid pilot View sample trust profile Join beta Analyze in workspace after signup

Beta and paid pilot access · Corporate-domain only · Demo values are illustrative · Validate critical findings before action.