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Introduction: The Cybersecurity Dilemma Nobody Has Fully Solved
When a critical software vulnerability is discovered, governments and infrastructure operators face the same urgent question: Who is exposed, and how quickly can the risk be reduced?
The answer sounds simple, but obtaining it is not.
To determine whether a dangerous flaw exists across pipelines, power plants, telecommunications networks, transportation systems, healthcare environments, or other critical infrastructure, government agencies may need companies to provide detailed vulnerability scans, software inventories, device information, network diagrams, and security configurations.
Those records can be extremely valuable for defenders. They can also be extremely valuable for attackers.
A vulnerability report may reveal which systems are connected, what software they run, where outdated technologies remain, and which defensive controls are missing. If that information is leaked, stolen, misused, or accessed by an unauthorized party, it could become a highly detailed roadmap for cybercriminals or state-backed threat actors.
This creates a difficult contradiction: organizations are asked to share sensitive information to reduce cyber risk, yet sharing that information may create new risks of its own.
A cryptographic technique known as zero-knowledge proofs, or ZKPs, may offer a different path. Instead of forcing companies to reveal their security data, ZKPs could allow them to prove that a specific security condition is true—or false—without exposing the sensitive information behind the answer.
The concept may sound abstract, but its potential impact is practical. It could change how governments assess national cyber exposure, how regulators verify compliance, and how critical infrastructure operators cooperate during major security events.
Original Summary: Trust the Answer Without Seeing the Data
The Central Challenge: Cybersecurity Information Is Too Valuable to Share Casually
For more than a decade, governments have encouraged organizations to share cybersecurity information. Legal protections, public-private partnerships, industry programs, and government-led information-sharing initiatives have helped companies exchange indicators of compromise, threat intelligence, incident details, and defensive guidance.
These efforts have produced meaningful improvements.
However, most information-sharing systems focus on what happens after suspicious activity or an attack is detected. They are less effective at encouraging companies to share highly sensitive information about vulnerabilities, weak security controls, and internal technology environments before an incident occurs.
The problem is straightforward: the information that would often be most useful to governments is also the information companies are most reluctant to disclose.
The Hidden Risk: Vulnerability Data Can Become an Attack Blueprint
A detailed vulnerability scan can reveal much more than whether a company has a security flaw.
It may identify connected devices, operating systems, software versions, exposed services, network relationships, configuration weaknesses, and missing security controls. In the wrong hands, this information could help an attacker prioritize targets and plan an intrusion.
Organizations also worry about what may happen after sensitive information leaves their control.
Could the data be breached?
Could it be shared with another government agency?
Could it be requested through legal processes?
Could it later be used in an enforcement action or regulatory investigation?
Could information provided voluntarily for national security purposes create unexpected legal or commercial consequences?
These concerns are not simply signs of corporate secrecy. They reflect the reality that sensitive cyber data can create risk long after it has been shared.
The Cryptographic Alternative: Prove the Result, Keep the Evidence Private
Zero-knowledge proofs offer a way to separate the answer from the underlying data.
Suppose a government agency asks an infrastructure operator:
Does a specific vulnerability exist anywhere within this defined group of systems?
Under a conventional reporting process, the company might send a vulnerability scan or software inventory to the government. The agency would then inspect the data and determine whether the vulnerable product or version was present.
With a zero-knowledge proof, the company could keep the scan inside its own environment.
A cryptographic system would evaluate the local data according to an agreed set of rules. It would then generate a mathematical proof showing that the answer is valid without revealing the device list, software inventory, network architecture, or full vulnerability report.
The government would receive the answer and the proof—not the sensitive records used to generate it.
How Zero-Knowledge Proofs Could Work in Cybersecurity
Step One: Define an Exact Security Question
The government and participating organization would first agree on a precise question.
For example:
Is CVE-2026-XXXX present on any internet-facing system within the organization’s authorized production environment?
The question must be clearly defined because the proof can only verify what the agreed rules actually measure.
Ambiguous questions create ambiguous results.
If the definition of “production system,” “internet-facing,” or “affected software” differs between organizations, the resulting proofs may not be comparable.
Step Two: Keep the Sensitive Data Inside the Organization
The company would collect or maintain its vulnerability scan, asset inventory, software information, or security-control evidence within its own protected environment.
The raw information would not be uploaded to a central government database.
This reduces the number of locations where highly sensitive infrastructure data is stored and limits the exposure created by transferring large volumes of security information between organizations.
Step Three: Evaluate the Data Locally
A trusted cryptographic application would examine the company’s authorized data according to the agreed rules.
For a vulnerability question, the system might compare:
Product names
Software versions
Vulnerability identifiers
Configuration requirements
Asset scope
Exposure conditions
The evaluation would occur locally, without requiring an outside agency to inspect the complete dataset.
Step Four: Generate a Mathematical Proof
The system would produce a cryptographic proof tied to the answer.
If the data demonstrates that the vulnerability is present, the proof would support a valid “yes” result.
If the organization claims the vulnerability is absent, the proof would need to demonstrate that the claim follows from the evaluated data and agreed rules.
The organization should not be able to generate a valid proof for a false answer while using the same trusted rules and the same authenticated dataset.
Step Five: Verify the Result Without Accessing the Scan
The government or authorized verifier would receive the proof and validate it.
The verifier would not need access to:
The complete vulnerability report
The organization’s device inventory
Internal IP addressing information
Network topology
Proprietary configurations
Sensitive operational details
The result could be trusted cryptographically without requiring the verifier to possess the underlying data.
A Simplified Conceptual Example
Local Security Evaluation
Sensitive vulnerability data remains inside the organization
scan_data="internal_vulnerability_scan.json"
Define the approved vulnerability question
target_vulnerability="CVE-2026-XXXX"
Evaluate local data
zk-security-check
–input $scan_data
–vulnerability $target_vulnerability
–scope authorized-production-assets
–generate-proof
–output security-proof.json
The command above is conceptual rather than a production-ready ZKP implementation. Its purpose is to illustrate how a local security tool might evaluate protected data and generate a proof without transmitting the original scan.
External Proof Verification
Government or authorized verifier checks the proof
zk-security-verify
–proof security-proof.json
–policy approved-vulnerability-rule.json
The verifier receives a mathematically verifiable result rather than the organization’s complete internal security records.
Deep Analysis: The Technology Is Powerful, but the Trust Model Is Hard
The Proof Is Only as Reliable as the Data
Zero-knowledge proofs can verify that a result follows from specific data and rules. They do not automatically prove that the underlying data is complete, current, accurate, or honestly collected.
This is one of the most important limitations.
A company could theoretically provide an incomplete asset inventory, exclude a high-risk environment, use an outdated scan, or manipulate the input before generating a proof.
The cryptography might work perfectly while the security conclusion remains misleading.
Therefore, a national ZKP-based reporting system would require strong controls around data collection, asset coverage, scan freshness, and evidence integrity.
Trusted Inputs May Matter More Than Advanced Mathematics
Governments may need mechanisms to verify where the data came from and whether it was altered.
Possible approaches could include:
Digitally signed scanner outputs
Hardware-backed attestation
Trusted execution environments
Authenticated asset inventories
Tamper-evident logs
Independent security assessments
Time-stamped scan records
Standardized collection tools
The proof can establish that an answer was calculated correctly. Additional controls are needed to establish that the underlying evidence deserves trust.
A Proof Can Answer the Wrong Question Perfectly
A mathematically valid proof is not necessarily a useful security result.
Imagine an agency asks:
Is the vulnerability present in systems scanned during the last 30 days?
The organization may generate a valid proof showing “no.”
However, the organization might have systems that were not included in the scan.
The proof would accurately answer the question but fail to represent the organization’s complete cyber exposure.
This means policymakers must carefully define both the question and the required data scope.
Standardization Will Be Essential
Different organizations use different scanners, asset-management platforms, operating environments, and security processes.
A proof generated from one company’s data may not be directly comparable to a proof generated elsewhere unless the system uses common definitions.
Standards may be needed for:
Vulnerability identification
Asset classification
Software naming
Version comparison
Scan freshness
System scope
Proof formats
Verification procedures
Evidence retention
Without common standards, ZKPs could create a collection of technically valid but operationally inconsistent results.
The Government Must Avoid Turning Privacy Technology Into a Surveillance Tool
The goal should be to reduce unnecessary data exposure—not to create a new mechanism for continuous infrastructure monitoring.
A carefully designed system should follow principles such as:
Ask only for necessary information
Use narrowly defined security questions
Avoid collecting unrelated operational data
Limit proof retention
Establish clear legal protections
Prevent secondary use without authorization
Maintain transparent governance
If organizations believe ZKPs are simply a more sophisticated method of regulatory surveillance, adoption may be slow.
Evidence From Early Testing
The Initial Experiment Produced Promising Results
The Foundation for Defense of Democracies’ Center on Cyber and Technology Innovation tested the approach using anonymized vulnerability information from three operational environments.
The experiment examined yes-or-no questions related to 38 known vulnerabilities while keeping the original scan data inside the participating environments.
Only the answers and cryptographic proofs were shared.
The results demonstrated that it was possible to identify how widely vulnerabilities appeared across the tested environments without transferring the full underlying vulnerability reports.
This is an important proof of concept.
However, a successful experiment is not the same as a mature national security system.
From Demonstration to Deployment
Real-world deployment would introduce challenges that controlled testing may not fully capture.
A production system would need to handle:
Large and constantly changing infrastructure
Millions of assets
Cloud environments
Operational technology
Legacy systems
Incomplete inventories
Third-party service providers
Rapidly evolving vulnerabilities
Emergency reporting requirements
Performance will matter.
A proof that takes hours to generate may be less useful during a fast-moving cyber crisis. A system must be secure, scalable, understandable, and practical for organizations with different levels of technical maturity.
The Best Next Step: Focused Pilots, Not Immediate Mandates
Start With Narrow, High-Value Questions
Government agencies should test zero-knowledge proofs through structured pilot programs rather than immediately requiring their use across critical infrastructure.
Early pilots could ask simple, measurable questions:
Is a specific high-risk vulnerability present?
Has a required security control been deployed?
Has multi-factor authentication been enabled for a defined system category?
Is a critical system running an approved software version?
Narrow questions would make it easier to evaluate proof accuracy, operational cost, privacy benefits, and implementation challenges.
CISA Could Coordinate Operational Testing
Cybersecurity and Infrastructure Security Agency could work with critical infrastructure operators to identify high-priority use cases and test the approach under realistic conditions.
CISA’s experience with cyber risk, vulnerability coordination, and public-private cooperation could help determine whether ZKPs provide meaningful improvements over existing reporting systems.
The agency could also assess how proofs might support emergency vulnerability response without requiring companies to submit highly sensitive infrastructure data.
NIST Could Define Technical Trust Requirements
National Institute of Standards and Technology could help establish technical standards for proof generation, verification, input integrity, and security assurance.
NIST could define what makes a cybersecurity proof trustworthy and what evidence should accompany it.
This would help prevent fragmented implementations that use incompatible rules or inconsistent security assumptions.
Regulators Could Reduce Compliance Friction
Regulatory agencies may eventually use privacy-preserving proofs to verify whether organizations meet specific cybersecurity requirements.
Instead of demanding large collections of internal records, regulators could request evidence that a defined control is present or a required security condition has been met.
This could reduce reporting burdens while limiting unnecessary exposure of proprietary and sensitive information.
However, regulators would still need mechanisms for audits, investigations, and exceptional cases where deeper evidence is necessary.
What Undercode Say:
A New Model for Cyber Trust
Zero-knowledge proofs may represent one of the most important ideas in cybersecurity governance because they challenge the assumption that trust requires full visibility.
For years, organizations have been told that sharing more information automatically creates better collective security.
That principle is only partly true.
More information can improve awareness, but it can also increase the number of systems capable of being breached.
A centralized database containing detailed infrastructure inventories could become an extremely attractive target.
The same information collected to protect critical infrastructure could be used to map and attack it.
Zero-knowledge proofs introduce a different principle: share the security conclusion, not necessarily the security blueprint.
This could reduce the conflict between corporate confidentiality and national cyber resilience.
Companies may become more willing to cooperate when they are not required to expose their entire technology environment.
Governments may gain faster answers without waiting for legal reviews and sensitive data-transfer agreements.
The approach could improve trust between public agencies and private infrastructure operators.
It could also reduce the long-term liability associated with storing large volumes of sensitive cyber information.
However, cryptography cannot solve weak governance.
A proof generated from incomplete data can produce a misleading conclusion.
A proof based on outdated scans may provide false confidence.
A proof designed around vague rules may answer the wrong question.
The most difficult challenge may not be building the mathematics.
The most difficult challenge may be deciding what should be measured and how the evidence should be trusted.
Cybersecurity leaders must avoid treating ZKPs as a magical replacement for audits.
They should be viewed as an additional verification tool.
Independent assessments may still be necessary.
Incident investigations may still require detailed evidence.
High-risk environments may still require direct inspection.
Zero-knowledge proofs are strongest when the question is narrow and the expected answer is clearly defined.
They may be especially useful during major vulnerability events.
Governments could quickly determine how many organizations are affected without collecting every company’s internal scan.
This could improve national-level risk awareness.
It could also help prioritize emergency support and remediation.
The technology may eventually support supply-chain security.
A software provider could prove that a product passed a required security check without revealing proprietary source code.
Cloud providers could prove that selected controls are active without exposing internal infrastructure.
Critical infrastructure operators could prove compliance while protecting sensitive operational details.
The long-term opportunity extends beyond vulnerability reporting.
ZKPs could become part of a broader privacy-preserving cybersecurity architecture.
But deployment must be cautious.
Poorly designed systems could create false confidence.
Complex proof systems may also be difficult for smaller organizations to operate.
Government pilots should measure cost, speed, accuracy, usability, and security.
The results should be transparent.
Standards should be developed collaboratively with industry.
Participation should initially be voluntary.
The goal should be practical security improvements—not cryptographic complexity for its own sake.
If implemented responsibly, ZKPs could make cyber cooperation more trustworthy.
They may help governments learn what matters without collecting everything.
That could become a major shift in how cyber risk is managed.
The future of security sharing may not depend on who holds the most sensitive data.
It may depend on who can prove the most important security facts while exposing the least.
✅ Zero-Knowledge Proofs Can Verify Claims Without Revealing Underlying Data
Zero-knowledge proofs are an established cryptographic concept designed to allow one party to demonstrate that a statement is true without revealing the secret information used to establish it.
Their application to cybersecurity reporting is technically plausible and increasingly relevant as organizations seek privacy-preserving verification methods.
However, implementation quality and the security of the proof system remain essential.
✅ Vulnerability Reports Can Contain Highly Sensitive Infrastructure Information
Vulnerability scans may reveal software versions, devices, exposed services, configuration weaknesses, and network relationships.
If compromised, such information could help attackers identify vulnerable targets and prioritize intrusion paths.
The risk depends on the level of detail and the security controls protecting the data.
✅ Privacy-Preserving Proofs Could Reduce Unnecessary Data Exposure
A properly designed ZKP system can reduce the need to transfer raw vulnerability data to external organizations.
This may lower the risks associated with centralized storage, unauthorized access, and secondary use of sensitive security records.
It does not eliminate risk because the original data and proof-generation environment must still be protected.
❌ A Valid Proof Does Not Automatically Guarantee Complete or Accurate Security Data
A zero-knowledge proof can demonstrate that a conclusion follows from the data and rules used.
It does not automatically prove that every asset was scanned or that the organization supplied complete information.
Trusted data collection and independent validation remain necessary.
❌ Zero-Knowledge Proofs Are Not a Complete Replacement for Cybersecurity Audits
Some security questions require detailed investigation, contextual analysis, technical evidence, or direct inspection.
ZKPs may reduce disclosure for narrowly defined checks, but they cannot replace every form of compliance review or incident response.
Their strongest role may be as a privacy-preserving layer within a larger security assurance framework.
Prediction
(+1) Privacy-Preserving Cyber Reporting Will Move From Research to Pilot Programs
Over the next several years, governments and critical infrastructure organizations are likely to test zero-knowledge proofs for narrowly defined cybersecurity questions.
Early adoption may focus on high-impact vulnerability checks, security-control verification, and sensitive compliance reporting.
As cyber threats increase and organizations become more cautious about sharing detailed infrastructure data, privacy-preserving verification may become more attractive.
The first successful deployments will probably remain limited in scope.
If pilots demonstrate that proofs are fast, reliable, affordable, and resistant to manipulation, adoption could expand across critical infrastructure and regulated industries.
The long-term impact may be significant: governments could gain faster visibility into systemic cyber risk while companies retain control over their most sensitive technical information.
The future of cyber cooperation may therefore move beyond a simple choice between secrecy and full disclosure.
It may become possible to prove that a system is secure—or prove that it is exposed—without revealing the digital blueprint behind the answer.
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