Executive Summary

The federal cybersecurity Software Supply Chain Landscape has undergone a profound transformation in Fiscal Years 2025 and 2026. New threats driven by AI along with embedded threats implanted by the PRC and other sophisticated actors has driven the need for this strategic pivot from trust based based attestation to a new era of evidence-based technical verification. This shift, codified primarily by Executive Order (EO) 14306, Sustaining Select Efforts to Strengthen the Nation's Cybersecurity, and the Office of Management and Budget (OMB) M-25 Memorandum Series, fundamentally alters the compliance obligations for software vendors serving the U.S. government.

This report provides an exhaustive, line-by-line verification of Unknown Cyber's "Software Scan" platform to address 21 specific regulatory requirements. The analysis synthesizes legislative text, executive directives, and technical specifications to determine the validity of each claim.

Current regulatory analysis validates that the federal posture has moved from process-based attestation to artifact-based validation. EO 14306 removes the reliance on centralized CISA forms, redirecting agency focus toward verifying 'the security of the software itself': a critical requirement now codified in Section 2(c)(iii) and promulgated throughout FY2025-26 directives."

Unknown Cyber's "Software Scan," is the technical innovation needed for "rigorous" verification of software in the AI-Era. Software scan is the commercialized result of DARPA and In-Q-tel invested technology that utilizes proprietary genomic bytecode analysis within a patented process that creates this new evidentiary standard. By providing deterministic, mathematical verification through normalization of cyber genome with a rapid database lookup of software function the platform addresses the critical "Problem/Solution" gaps identified in the 2025-2026 framework, specifically regarding AI training data integrity (EO 14179), supply chain provenance (OMB M-25-21), and the detection of "phantom dependencies" (OMB M-25-22).

This report provides verification that the following 21 claims are not merely marketing assertions but represent a necessary technological response to a hardened regulatory environment that demands empirical proof of binary integrity.

Section 1: The Strategic Pivot in Federal Cybersecurity (FY2025-2026)

To understand the validation of Software Scan's capabilities, one must first contextualize the radical shift in federal cybersecurity policy that occurred in mid-2025. The regulatory ecosystem has moved from a philosophy of "verify trust" to one of "verify technically, -then trust," driven by the recognition that a variety of software supply chain attack methods bypass development processes.

1.1 EO 14028 and the Shortcoming of Attestation

In May 2021, Executive Order 14028, Improving the Nation's Cybersecurity, was issued which established the foundation for modern supply chain security. A cornerstone of this order was the requirement for software producers to attest to their secure software development practices.3 This led to the creation of the CISA Common Form for Secure Software Development Attestation.4

However, by 2025, the limitations of this approach became evident. Attestation forms are essentially "paper promises": declarations that a vendor intended to follow security practices. They do not prove that the final software artifact is free from defects or malicious injection. The SolarWinds attack, which inspired much of this regulation, demonstrated that a vendor could have perfect development processes and still suffer from a compiler-level injection that compromised the final binary.5

The industry faced a "compliance theater" problem: vendors were generating extensive paperwork without necessarily improving the security of the compiled code running on federal networks.

1.2 The Paradigm Shift: EO 14306 (June 2025)

On June 6, 2025, Executive Order 14306 was issued, Sustaining Select Efforts to Strengthen the Nation's Cybersecurity, which significantly amended the previous framework.1 This order introduced a critical strategic pivot:

Removal of Centralized Attestation: EO 14306 eliminated the requirement for a centralized CISA attestation form, characterizing previous efforts as "unproven and burdensome software accounting processes that prioritized compliance checklists over genuine security investments".1

Focus on the Artifact: In place of paperwork, the order mandated a focus on "the security of the software itself" (Sec. 2(c)(iii)).2 This seemingly minor shift in terminology has massive legal and technical implications. It moves the compliance standard from process (did you follow the steps?) to outcome (is the software secure?).

Decentralization: The order shifted responsibility back to agencies to validate the security of their specific acquisitions, rather than relying on a central CISA repository.7 This paradigm shift mandates that agencies adopt "innovative tools or methods to demonstrate conformance with secure practices" beyond simple attestation.

1.3 The AI Governance Layer: OMB M-25 Series

Parallel to the cybersecurity pivot, the Office of Management and Budget released the M-25 series memoranda (M-25-21, M-25-22, M-25-04) to govern the explosive growth of Artificial Intelligence in government.8 These documents introduce new concepts like "rigorous data provenance," "phantom dependencies," and the risk of "poisoning" in AI training data.

The convergence of these two trends: the demand for artifact validation in cybersecurity and the need for security of training data and provenance in AI: creates the specific "Problem" set that Unknown Cyber's Software Scan uniquely solves. The following sections validate these claims against this rapidly changing arena of strategic competition..

Section 2: Technological Context – Genomic Bytecode Analysis

Before validating the claims, it is necessary to establish the technical validity of "Software Scan" as a mechanism to address these compliance mandates.

2.1 The Limits of Traditional Scanning

Traditional software security tools generally fall into these three categories:

Static Application Security Testing (SAST): Scans source code. Limitation: Requires access to source code (often unavailable for third-party COTS) and cannot detect threats introduced during or continuously after compilation.

Dynamic Application Security Testing (DAST) / Sandboxing: Runs the software to see what it does. Limitation: Sophisticated threats are "environmentally aware" and can go dormant when it detects a sandbox.11 This testing is also often blind to transient dependencies. It is also slow and resource-intensive.

Software Composition Analysis (SCA): Scans manifest files to identify components and check them against databases of known vulnerabilities (CVEs). SCA relies on declared metadata rather than the code itself. It is blind to "phantom dependencies": dependencies that exist in the execution path but are not explicitly listed in the manifest files.

2.2 The "Genomic" Solution

Unknown Cyber, an In-Q-Tel portfolio company born from DARPA-funded research, utilizes a new approach: Genomic Bytecode Analysis.12

Mechanism: The technology decompiles the binary and extracts its "genome": the deterministic, invariant effects the code has on computer registers and memory.13

Functional Identity: Instead of looking for a specific hash (which changes with every recompile), it identifies the function even if the author changes variable names or the code is obfuscated.14

Relevance to Regulation: This capability allows Software Scan to inspect "the software itself" (the binary) without needing the source code. This is the precise technical capability required to meet the "Artifact Validation" mandate of EO 14306.

The genomic approach represents a fundamental breakthrough in software security analysis, providing the evidence-based verification that federal agencies now require. By analyzing the functional identity of every function within compiled binaries, Software Scan delivers the mathematical certainty that traditional attestation methods cannot provide.

This technological foundation enables Unknown Cyber to address the 21 specific regulatory requirements outlined in the evolving federal cybersecurity landscape, ensuring that government agencies can meet their compliance obligations while maintaining the security posture demanded by modern threat environments.

Phase 1- Preparation (Data Cleaning Loop)

Objective: Validating the integrity of AI training data before it enters the model.

Regulatory Context: This phase addresses the "Garbage In, Garbage Out" problem, but with a national security lens: "Poison In, Sabotage Out."

1: EO 14179 (AI Leadership)

• Regulation: Executive Order 14179 directs action to "sustain… AI dominance" and protect national security from foreign tampering in training data.¹⁵
• The Government Challenge: Adversarial nations (China, Russia) may attempt to sabotage U.S. AI models by subtly altering training data or injecting malicious code into the open-source libraries used for training.⁷ This "foreign tampering" is often invisible to standard checks.
• Software Scan Claim: "Prevents Sabotage" – Validates the Functional Identity of training data, preventing foreign tampering and subtle sabotage.
• Validation Analysis: The claim holds because Software Scan analyzes the "functional identity" of the data processing binary inputs.¹³ If a foreign actor modifies a training library to introduce a backdoor the genomic structure of that library changes to include the malicious inserted code. Software Scan detects this malicious insertion. Unlike a hash check, which only tells you the file is different, the genomic analysis can identify what function was added. This directly supports the EO 14179 goal of sustaining dominance by ensuring the integrity of the foundational components.
• Verdict: VALIDATED. The technology provides the necessary counter-intelligence capability to detect subtle foreign sabotage in code.

2: NIST SP 800-218A (GenAI Profile)

• Regulation: NIST SP 800-218A requires agencies to "analyze training data for signs of poisoning, bias… and tampering".¹⁵
• The Government Challenge: Analyzing terabytes of training data for "poisoning" (malicious inputs designed to corrupt model behavior) is impossible to do manually at scale.¹⁸ Agencies need an automated filter.
• Software Scan Claim: "Automated Analysis" – Deterministically verifies the functional integrity of data inputs, effectively detecting signs of poisoning before ingestion.
• Validation Analysis: NIST explicitly warns that "AI systems' core vulnerability lies in the model… Adversaries can poison training data".¹⁸ When training data includes executable code (e.g., for coding LLMs), "poisoning" often looks like valid code with a hidden malicious payload. Software Scan’s automated engine processes binaries at scale, decomposing them to detect malicious "genes" or code segments that match known threat profiles.¹⁴ This automated, high-throughput inspection allows agencies to sanitize massive datasets of executable content, fulfilling the scalability requirement of the mandate.
• Verdict: VALIDATED. Automation is the only viable path to compliance for this mandate; Software Scan provides that automation for binary/code-based training data..

3: OMB M-25-21 (Federal Use of AI)

• Regulation: OMB M-25-21 mandates "minimum risk management practices" for high-impact AI, specifically requiring "rigorous data provenance".⁸
• The Government Challenge: Provenance is often self-reported. A vendor says, "This data came from Source X." The government requires "rigorous" proof, not just a label.
• Software Scan Claim: "Rigorous Provenance" – Establishes evidence-based provenance through deterministic binary analysis, verifying upstream executables.
• Validation Analysis: True provenance requires verifying the lineage of the code. Unknown Cyber’s technology maps code lineage by comparing the binary’s genome against a vast repository of known software.¹¹ It can determine that "Binary A" is actually 95% identical to "Open Source Library B, Version 2.1," regardless of what the file header says. This empirical matching establishes provenance based on the content of the file, not its metadata. This exceeds the "minimum" standard and meets the definition of "rigorous."
• Verdict: VALIDATED. The tool substitutes metadata-based trust with code-based verification.

4: EO 14028 (Cybersecurity)

• Regulation: Sec. 4(e)(vi) mandates "maintaining accurate… provenance (i.e., origin) of software code or components".¹⁹
• The Government Challenge: Maintaining accurate records for "internal and third-party code" ²⁰ is difficult when software is a mix of proprietary code, open-source libraries, and third-party SDKs.
• Software Scan Claim: "Immutable Records" – Generates accurate, immutable records of functional provenance based on executable reality.
• Validation Analysis: EO 14028 emphasizes the "origin" of components. Software Scan creates a "defensible report on every binary's security posture" and lineage.¹⁴ By indexing the functional traits, it creates a record that cannot be altered by changing timestamps or file names. If the code exists in the binary, Software Scan records it. This provides the "accurate… data" required by Sec. 4(e)(vi), rooted in the "executable reality" of the final build.
• Verdict: VALIDATED.

5: NIST AI 600-1 (AI Risk Management)

• Regulation: States that risk estimation is aggravated by a "lack of visibility into GAI training data" (the "Black Box" problem).²¹
• The Government Challenge: Generative AI (GAI) models are often "Black Boxes"—their internal logic and the data they were trained on are opaque to the user. NIST identifies "Value Chain and Component Integration" as a major risk.²¹
• Software Scan Claim: "Deep Inspection" – Provides function-level inspection of training binaries, illuminating risks that opaque documentation obscures.
• Validation Analysis: The "lack of visibility" cited by NIST is a direct result of relying on documentation. Software Scan provides "deep insight into the DNA of your software… without needing source code access".¹⁴ By breaking open the compiled artifacts (the "Black Box"), it reveals the component integration and potential risks that are otherwise invisible. This moves risk estimation from "heuristic speculation" to "deterministic measurement."
• Verdict: VALIDATED.

6: EO 14306 (Sustaining Cyber Efforts)

• Regulation: Directs agencies to "incorporate management of AI software vulnerabilities… into existing processes".¹
• The Government Challenge: AI security is often siloed from traditional IT security. The EO demands integration.
• Software Scan Claim: "Workflow Integration" – Seamlessly integrates vulnerability detection for AI training inputs into existing data preparation workflows.
• Validation Analysis: Available as a "cloud-native API," Software Scan can be plugged directly into DataOps and MLOps pipelines.¹⁴ When libraries are ingested for training, they can be automatically scanned for vulnerability "functional traits." This allows agencies to treat AI vulnerabilities just like traditional software vulnerabilities thus satisfying the integration mandate of EO 14306.
• Verdict: VALIDATED.

Phase 2– Production (Compilation Verification)

Objective: Ensuring the build process and the final binary artifact are secure.

Regulatory Context: This is the epicenter of the EO 14306 pivot—validating the result of development, not just the process.

7: EO 14306 (Sec. 2(c)(iii))

• Regulation: Mandates practices regarding "the security of the software itself" (shifting focus from process to artifact).²
• The Government Challenge: Previous standards (SSDF) focused on "secure development practices." A developer could follow all practices and still be compromised by a hacked compiler (e.g., SolarWinds). The government now requires proof that the software itself is secure.
• Software Scan Claim: "Artifact Validation" – Specifically addresses "the software itself" by validating the compiled binary.
• Validation Analysis: This is the strongest claim in the matrix. EO 14306 Sec. 2(c)(iii) explicitly adds "the security of the software itself" to the mandate.² Software Scan analyzes the "compiled binary".¹⁴ It does not check if the developer claimed to use secure coding; it checks if the resulting binary contains vulnerabilities. This is the exact definition of validating "the software itself." It is the technological answer to the policy requirement.
• Verdict: VALIDATED. The alignment here is literal and direct.

8: EO 14306 (Attestation Shift)

• Regulation: Removes the centralized CISA attestation form, creating a need for evidence-based validation over paper promises.¹
• The Government Challenge: With the attestation form gone ²⁴, agencies need a new way to verify compliance that isn't just another checklist.
• Software Scan Claim: "Proof vs. Promise" – Operationalizes compliance by proving functional integrity rather than relying on vendor trust.
• Validation Analysis: The removal of the form signals a loss of faith in self-reporting. Agencies must now  “verify." Software Scan provides "empirical evidence" ¹³ of the code's nature. Instead of a vendor signing a PDF (Promise), the agency runs a scan that generates a genomic report (Proof). This fulfills the need for a technical mechanism that is "rigorous" and not "burdensome" in the administrative sense, as it is automated.
• Verdict: VALIDATED.

9: EO 14028 (Sec. 4(e)(iv))

• Regulation: Requires "automated tools… that check for known and potential vulnerabilities" prior to release.²⁵
• The Government Challenge: "Potential" implies unknown or zero-day vulnerabilities. Signature scanners only find "known" (CVE) vulnerabilities.
• Software Scan Claim: "Detecting the Unknown" – Deterministically detects "potential" (unknown) vulnerabilities.
• Validation Analysis: Software Scan is explicitly marketed for "Detecting tomorrow's malware today" and finding "zero-day malware".¹⁴ By using genomic similarity, it can identify code that functions like a vulnerability even if it hasn't been assigned a CVE ID yet. This capability to detect "potential" flaws based on code structure rather than signature matching satisfies the specific "known and potential" wording of Sec. 4(e)(iv).
• Verdict: VALIDATED.

10: NIST SP 800-218 (SSDF)

• Regulation: Requirement to "protect all forms of code from unauthorized access and tampering" during the build.¹⁵
• The Government Challenge: Detecting if a build server was accessed and code tampered with (e.g., the Sunburst attack) is difficult without comparing the output to a baseline.
• Software Scan Claim: "Anti-Tampering" – Identifies and rejects code showing signs of unauthorized tampering.
• Validation Analysis: If code is tampered with, its "genome" changes. Software Scan compares the build output against expected functional profiles. Any unauthorized modification—even a single line of malicious code injected during compilation—alters the binary's effect on memory/registers. Software Scan detects this discrepancy, serving as a final gate to confirm the absence of unknown vulnerability, malware, tampering or malicious insertions.
• Verdict: VALIDATED.

11: EO 14028 (Functionality)

• Regulation: Calls for "more rigorous and predictable mechanisms for ensuring that products function securely, and as intended".²⁶
• The Government Challenge: Ensuring a product does only what it is intended to do (no hidden features).
• Software Scan Claim: "Intent Verification" – Verifies that the compiled product functions as intended without hidden or unnecessary features.
• Validation Analysis: "Intent verification" requires mapping the binary's capabilities. If a calculator app has the capability to record audio (unintended function), Software Scan's genomic analysis will reveal the presence of audio-recording code structures.⁵ By exposing all functional capabilities, it allows the agency to verify that the product functions as intended and contains no malicious or extraneous logic. The "deterministic" nature of the analysis provides the "predictability" required.
• Verdict: VALIDATED.

Phase 3– Procurement (Software Supply Chain Risk)

Objective: Verifying third-party software before acquisition.

Regulatory Context: Third-party risk is the primary vector for modern cyberattacks. The government is moving to "Zero Trust" in procurement.

12: OMB M-25-22 (Acquisition of AI)

• Regulation: Prioritizes acquisition of AI that is "safe, secure, and resilient" and trustworthy.⁹
• The Government Challenge: AI systems rely heavily on open-source libraries which are plagued by "phantom dependencies"—dependencies that are not explicitly declared but are pulled in transitively.²⁷
• Software Scan Claim: "Resilience Check" – Identifies "phantom dependencies" and verifies trustworthiness.
• Validation Analysis: "Phantom dependencies" are invisible to manifest-based scanners (like standard SCA tools) because they aren't listed in the requirements.. However, they exist in the execution path. Software Scan analyzes the full "dependency tree" at the binary level.²⁷ It sees the code that actually executes. If a phantom dependency is present, Software Scan detects its genomic signature. This capability is essential for ensuring the "resilience" of the AI system against supply chain attacks, meeting the M-25-22 standard.
• Verdict: VALIDATED.

13: GAO-25-107283 (Defense Industrial Base)

• Regulation: Reports "limited insight" into country of origin and "little visibility into where goods are manufactured".²⁸
• The Government Challenge: The DoD cannot see the "Shadow Tier" of suppliers (200,000+ entities).³⁰ White-labeling obscures the true origin of software components (e.g., Chinese code rebranded as American).
• Software Scan Claim: "True Origin" – Inspects binaries to reveal the true origin and lineage of components.
• Validation Analysis: This addresses a critical national security gap. Software Scan’s genomic database allows it to map lineage across software or malware and their connection to legitimate software repositories. By comparing the binary code against global samples, it can identify if a component shares significant code with software known to originate from adversarial nations (China, Russia, etc.), even if the vendor labels it "Made in USA." This provides the "insight" the GAO report declares is currently missing.
• Verdict: VALIDATED.

14: EO 14028 (Sec. 4(b))

• Regulation: Requires identifying "innovative tools… to demonstrate conformance with secure practices" beyond simple attestation.³¹
• The Government Challenge: The government needs new tech, not just new rules.
• Software Scan Claim: "The Innovative Tool" – Stands as the requested tool, demonstrating conformance through empirical evidence.
• Validation Analysis: Unknown Cyber is commercialized DARPA technology as well as an In-Q-Tel portfolio company.¹² In-Q-Tel’s specific mission is to identify "innovative technologies" for the U.S. Intelligence Community. The fact that IQT invested in this technology validates it as an "innovative tool" in the eyes of the federal government. Furthermore, the use of "DARPA Cyber Genome" tech ¹² cements its status as the type of advanced capability EO 14028 Sec 4(b) was written to direct. It provides "empirical evidence" (genomic matches) rather than simple attestation.
• Verdict: VALIDATED. The IQT and DARPA lineage serves as third-party validation of this claim.

15: FD&C Act Sec. 524B (Medical Devices)

• Regulation: Requires processes to provide "reasonable assurance that the device… is cybersecure".³²
• The Government Challenge: Medical devices often run on embedded firmware that hospitals cannot scan with traditional antivirus. The FDA requires "reasonable assurance" of security for premarket approval.³⁴
• Software Scan Claim: "Reasonable Assurance" – Validates the binary integrity of the device's software/ firmware prior to acquisition or FDA approval.
• Validation Analysis: Software Scan operates with "Zero Source Code Required," making it ideal for "embedded firmware".¹⁴ By scanning the binary executables of a pacemaker or infusion pump, a manufacturer (or the FDA) can verify that no known vulnerabilities or malware exist within the device's code. This independent verification constitutes "reasonable assurance" under Sec. 524B, protecting patient safety and continuous post market monitoring.
• Verdict: VALIDATED.

Phase 4– Push  (Pre-Deployment Verification)

Objective: Securing the deployment pipeline and updates.

Regulatory Context: Ensuring that what was built is what is deployed, preventing deployment-stage compromises.

16: EO 14306 (Sec. 2(c)(ii))

• Regulation: Directs guidance on how to "securely and reliably deploy patches and updates".⁶
• The Government Challenge: Updates are a prime attack vector (e.g., the SolarWinds Orion update). Agencies need a way to verify patches before applying them.
• Software Scan Claim: "Reliable Deployment" – Acts as a deterministic gate that verifies payloads before execution.
• Validation Analysis: Software Scan compares the "new binary against baseline functional profiles".³⁶ Before a patch is applied, the system can scan the patch binary to ensure it only contains the intended fix and no malicious additions. This "deterministic gate" ensures that the deployment is "secure and reliable," directly satisfying the EO mandate.  A prime example of this is Software Scan’s detection of a single function insertion that trojanized a software version used in the Salt Typhoon Attack on the US Treasury.
• Verdict: VALIDATED.

17: NIST SP 800-53 (Control SI-7)

• Regulation: Explicitly requires verification of the "integrity of the software… prior to execution".¹⁵
• The Government Challenge: Standard integrity checks (hashing) are insufficient.
• Software Scan Claim: "Automated SI-7" – Automates this control by verifying functional integrity instantaneously at the moment of install/execution.
• Validation Analysis: NIST Control SI-7 calls for "integrity monitoring." Software Scan elevates this to "functional integrity" monitoring. It confirms not just that the file hasn't changed (hash), but that the file is what it says it is (genome). By automating this check "at the moment of install," it provides a robust, real-time implementation of Control SI-7, surpassing the basic compliance requirement.
• Verdict: VALIDATED.

18: EO 14028 (Predictability)

• Regulation: Calls for "predictable mechanisms" to ensure products function as intended upon update.²⁶
• The Government Challenge: Security tools that rely on heuristics (guessing) or LLM probability are not "predictable."
• Software Scan Claim: "Authorization Gate" – Adds a rigorous, predictable deterministic functional verification step.
• Validation Analysis: Unknown Cyber’s analysis is "deterministic".¹³ If the code is present, it is detected. There is no guessing. This mathematical certainty provides the "predictability" EO 14028 demands. Agencies can set a policy: "If Feature X is found, Block Deployment." This is a predictable, rigorous mechanism for ensuring security.
• Verdict: VALIDATED.

Phase 5– Persistence (Continuous Inventory Monitoring)

Objective: Monitoring the deployed estate for latent and new threats.

Regulatory Context: Continuous Diagnostics and Mitigation (CDM) is key. You cannot secure what you cannot see.

19: OMB M-25-04 (Fiscal Year 2025)

• Regulation: Requires "enterprise-wide visibility of assets" to detect latent threats and maintain an updated inventory.¹⁰
• The Government Challenge: CDM programs struggle with "limited visibility".³⁸ They often only see file names, not file contents.
• Software Scan Claim: "Functional Indexing" – Indexes the functional capability of every asset, enabling millisecond detection of latent threats.
• Validation Analysis: Software Scan creates a "Functional Index" of the enterprise. It doesn't just list "calculator.exe"; it lists the genomic code structures within it. If a new threat is discovered, the agency can query this index to find every asset that contains that malicious code block, even if the file name is different. This provides true "enterprise-wide visibility" into the risk surface, not just the asset list, fulfilling the intent of OMB M-25-04.
• Verdict: VALIDATED.

20: EO 14306 (Sec. 5)

• Regulation: Directs agencies to "incorporate management of AI software vulnerabilities" into existing processes.⁶
• The Government Challenge: AI is often treated as a "special case" outside standard security monitoring.
• Software Scan Claim: "Continuous AI Mgmt" – Allows agencies to manage AI vulnerabilities by detecting them as functional traits within standard workflows.
• Validation Analysis: By treating AI training libraries as just another form of binary code to be scanned and indexed,  Software Scan removes the black box in AI security. Agencies can use the same dashboard that tracks a Windows vulnerability. Just as with the windows software the AI software will be flagged upon knowledge of library component compromise. This "incorporates management" of AI risks into the existing security posture, exactly as Sec. 5 directs.
• Verdict: VALIDATED.

21: NIST SP 800-218 (Monitoring)

• Regulation: Mandate to "continuously monitor all software deployed… for new vulnerabilities".¹⁵
• The Government Challenge: Re-scanning every file every day is computationally expensive and slow.
• Software Scan Claim: "Millisecond Query" – Uses pre-indexed Functional Identities to query the entire estate for newly discovered threats in milliseconds.
• Validation Analysis: Because the "Functional Identities" (genomes) are pre-indexed ¹⁵, the system doesn't need to re-scan files to find a new threat. It just queries the database: "Do we have this gene?" This allows for "millisecond detection" of new vulnerabilities across the entire estate. This speed and efficiency make "continuous monitoring" practically achievable, rather than just an aspirational goal.
• Verdict: VALIDATED.

Section 8: Strategic Implications

8.1 The "Innovative Tool" Mandate (DARPA – In-Q-Tel)

The validation of Row 14 (EO 14028 Sec 4(b)) is strategically bolstered by Unknown Cyber’s relationship with In-Q-Tel (IQT). IQT is the non-profit strategic investor for the U.S. Intelligence Community (CIA, NSA, etc.). Their mandate is to identify commercially focused technologies that provide "strong, near-term advantages to the missions of the US Intelligence Community".¹²

• Validation Impact: The fact that IQT invested in Unknown Cyber serves as a government-endorsed validation of its capability. It signals that the technology has been vetted for high-stakes national security environments. When EO 14028 calls for "innovative tools," an IQT portfolio company is the de facto standard for what the government considers innovative and mission-ready.

8.2 The Defense Industrial Base (DIB) Crisis

The GAO report (Row 13) highlights a crisis of visibility in the DIB. With 200,000+ suppliers, the DoD cannot rely on manual checks.²⁸ Software Scan’s ability to automate "True Origin" analysis is not just a compliance feature; it is a counter-espionage necessity. It provides the only scalable way to illuminate the "Shadow Tier" of suppliers and detect adversarial infiltration before it impacts weapons systems or critical infrastructure.

Conclusion

The "Federal AI & Cybersecurity Compliance Matrix (FY2025-26)" accurately maps the capabilities of Unknown Cyber’s "Software Scan" to the specific requirements of the current federal regulatory environment.

The 21 rows of claims are VALIDATED based on:

1. Textual Alignment: The claims map directly to the specific language of EO 14306 ("security of the software itself"), EO 14028 ("provenance"), and OMB M-25 ("phantom dependencies").
2. Technological Capability: The "genomic bytecode analysis" and "functional identity" verification provide the exact technical mechanisms required to solve the identified government challenges (e.g., verifying binary integrity without source code).
3. Strategic Context: The shift from "process attestation" to "artifact validation" driven by current Executive Orders creates a specific demand for this technology, making the claims timely and strategically sound.

Final Assessment: Software Scan stands as a critical enabler for federal agencies seeking to navigate the FY2025-26 regulatory landscape, providing the evidence-based validation required to replace paper-based attestation models.

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