In March 2023, a group of senior engineers at Samsung's semiconductor division needed to debug faulty source code. Rather than wait for an internal process, they pasted the code directly into ChatGPT. A second engineer did the same with proprietary software for detecting defective manufacturing equipment. A third recorded a confidential internal meeting, transcribed it, and uploaded the full document to generate meeting minutes [5]. Three separate incidents. Three different engineers. All within weeks. All using a platform with no contractual data protections. Samsung's most sensitive semiconductor IP was now on OpenAI's servers [5].

Samsung banned all generative AI tools company-wide. JPMorgan restricted ChatGPT across the entire firm. Bank of America, Goldman Sachs, Citigroup, Deutsche Bank, and Wells Fargo followed within weeks [6]. Apple restricted ChatGPT and GitHub Copilot simultaneously [6]. The industry's knee-jerk reaction to Samsung's incident was to ban AI tools outright.

Three years later, the Netskope Cloud and Threat Report 2026 reports that 47% of employees who use AI tools at work do so through personal, unmanaged accounts, that the average enterprise runs 1,200 unofficial AI applications, and that 86% of organizations have no visibility into what those sessions contain [2]. The bans did not work. The behavior is now the default. And the financial damage has compounded: shadow AI now adds an average of $670,000 to breach costs, $19.5 million in annual insider risk per large organization, and touches 20% of all enterprise breaches [3] [4].

VectorCertain LLC is releasing this analysis to document why the ban-first approach to shadow AI governance is architecturally inadequate, why the data exfiltration channel it creates maps precisely to documented MITRE ATT&CK techniques, and how SecureAgent's four-gate pre-execution governance pipeline would have blocked every documented shadow AI data exfiltration event — before the paste, not after the breach [7].

• Scale of Shadow AI: 47% of employees use AI tools through personal, unmanaged accounts; average enterprise runs 1,200 unofficial AI applications; 86% of organizations have no visibility into AI data flows [1] [2]

• Financial Cost: Shadow AI adds $670,000 per breach; $19.5 million in annual insider risk per organization; 20% of enterprises have suffered a breach caused specifically by shadow AI [3] [4]

• Governance Gap: 97% of organizations that experienced an AI-related breach had no proper AI access controls; 63% had no AI governance policy at all [3]

• Validation Depth: 4 frameworks — 278 CRI diagnostic statements + 230 FS AI RMF COs + 11,268 ER7++ sprint tests (0 failures) + 14,208 ER8 trials (TES 98.2%) [7] [9]

• SecureAgent Result: Pre-execution output classification blocks proprietary data from reaching unauthorized AI endpoints — false positive rate 1 in 160,000; zero exfiltration confirmed [7]

VectorCertain LLC is the only company in the world that has independently validated — across 4 frameworks spanning the CRI Profile v2.1's 278 cybersecurity diagnostic statements, the U.S. Treasury FS AI RMF's 230 control objectives, MITRE ATT&CK ER7++ sprint results (11,268 tests, 0 failures), and MITRE ATT&CK ER8 self-evaluation (14,208 trials, TES 98.2%) — that its SecureAgent platform would have classified, flagged, and blocked the proprietary data exfiltration documented in every major shadow AI incident on record before it reached an unauthorized AI endpoint [7] [9] [8]. Samsung's engineers pasted semiconductor source code into ChatGPT in 2023. The bans that followed did not work — Netskope's 2026 Cloud and Threat Report confirms that 47% of employees still use personal AI accounts at work, creating an exfiltration channel that no firewall, DLP tool, or AI governance policy can see [2]. SecureAgent's Gate 3 (TEQ-SG) classifies every output action against a data taxonomy that operates independently of the employee's intent — blocking the paste before it executes, not after the IP is gone.

The Samsung incident of 2023 was more than just a rare occurrence; it signaled a broader trend. The AIUC-1 Consortium briefing — developed with input from Stanford's Trustworthy AI Research Lab and more than 40 security executives including CISOs from Confluent, Elastic, UiPath, and Deutsche Börse — documents the full scale of shadow AI exposure as it stands in 2026 [1]:

• 63% of employees who used AI tools in 2025 pasted sensitive company data — including source code and customer records — into personal chatbot accounts [1]

• 47% of employees use AI tools through personal, unmanaged accounts outside any organizational visibility [2]

• 86% of organizations report no visibility into their AI data flows [1]

• 64% of companies with annual revenue above $1 billion have lost more than $1 million to AI failures [1]

• 97% of organizations that experienced an AI-related breach had no proper AI access controls in place [3]

• 69% of organizations already suspect or have evidence that employees are using prohibited public generative AI tools, per Gartner's 2025 analysis of 302 cybersecurity leaders [10]

The data that flows through these unsanctioned sessions is not low-risk productivity content. Per LayerX research cited in the IBM data, employees are submitting: revenue figures, margin analysis, acquisition targets, compensation data, investor materials, customer records containing PII, source code, product roadmaps, manufacturing processes, employment contracts, pending litigation details, and settlement terms [6]. Every category represents a potential HIPAA violation, a PCI-DSS incident, a GDPR breach, a securities law exposure, or a trade secret loss.

"This combination of novel AI-driven threats and legacy security concerns defines the evolving threat landscape for 2026. Many employees continue using AI tools through personal accounts that lack proper security guardrails and fall outside the purview of their organizations' IT teams — creating opportunities for hackers to manipulate those tools and breach corporate networks."

— Netskope, Cloud and Threat Report 2026 [2]

Shadow AI data exfiltration does not require a malicious actor. It requires only an employee, a workflow problem, and a browser tab. But the data loss it produces maps precisely to documented MITRE ATT&CK techniques — and in the case of adversarial shadow AI manipulation, it enables nation-state-grade exfiltration through a channel that carries no malicious signature whatsoever [8]:

Technique 1 — T1567.002: Exfiltration Over Web Service: Exfiltration to Cloud Storage (Exfiltration)

• What happened: Employees upload proprietary source code, meeting transcripts, customer records, and financial data to consumer AI platforms via standard HTTPS — the same protocol as authorized business traffic; no network anomaly is generated

• DLP/security verdict: Standard web traffic. Encrypted. No signature. No alert.

Technique 2 — T1213: Data from Information Repositories (Collection)

• What happened: Before pasting to AI tools, employees access internal code repositories, CRM systems, legal databases, and EHR systems to retrieve the data they intend to submit — each access using valid credentials generating no anomaly

• DLP/security verdict: Legitimate internal access. No alert.

Technique 3 — T1552: Unsecured Credentials (Credential Access)

• What happened: 45.6% of teams use shared API keys for agent authentication; employees pasting API keys and tokens into AI tools to generate integration code expose machine credentials alongside human IP — a secondary exfiltration layer invisible to traditional monitoring

• DLP/security verdict: No malicious file. No anomalous process. No alert.

Technique 4 — T1048: Exfiltration Over Alternative Protocol (Exfiltration)

• What happened: AI-enabled shadow tools act as persistent data channels — employees using the same AI tool daily create an ongoing exfiltration pipeline that accumulates sensitive data across sessions, none of which is visible in any security dashboard

• DLP/security verdict: Authorized user. Authorized application (from the tool's perspective). No alert.

Technique 5 — T1078: Valid Accounts (Persistence / Defense Evasion)

• What happened: Every shadow AI session is authenticated with a valid employee credential — the same credential used for authorized work. The session is indistinguishable from legitimate activity. There is no lateral movement, no privilege escalation, and no network anomaly to detect

• DLP/security verdict: Valid account. Routine session. No alert across all vendors.

"What most teams miss: this is not malware, and it is not phishing. It is an OAuth-connected, workplace-integrated AI moving data laterally without triggering alerts. Employees are not trying to expose the organization. The models they use simply do not know what should be obvious."

— Reco, AI & Cloud Security Breaches: 2025 Year in Review [11]

The Samsung response — ban the tools — has been replicated by every major financial institution, healthcare system, and technology company that discovered the problem. The industry consensus response to shadow AI is: prohibit it. Three years of evidence demonstrates that prohibition does not work [6].

Four structural reasons current tools are incapable of preventing shadow AI data exfiltration:

• DLP cannot classify what it cannot see. Traditional data loss prevention tools monitor known channels — email, file transfers, authorized SaaS platforms. Consumer AI tools accessed through personal accounts are invisible to enterprise DLP by design. The session is encrypted. The tool is not on the approved list. The traffic looks identical to any HTTPS web session.

• Policy cannot enforce what employees don't perceive as risk. Research consistently shows that employees adopt shadow AI because it solves real workflow problems. Samsung's engineers were not acting recklessly — they were trying to debug code faster. While logical for employees, this behavior is disastrous for organizations. Telling employees not to use AI tools they find indispensable has a documented effect: they use them anyway, with slightly more caution [6].

• Bans create shadow usage, not compliance. Nearly half of employees would continue using personal AI accounts even after an organizational ban, per Healthcare Brew 2026 research [10]. Prohibition drives shadow AI deeper underground rather than eliminating it — replacing visible usage with usage that is even less traceable.

• The exfiltration channel is the enterprise data pipeline. The same organizational systems that make AI tools useful — access to code repositories, CRM data, patient records, financial systems — are the systems that create the exfiltration risk. You cannot deny employees access to their work systems. You can govern what they do with that access.

MITRE ATT&CK Enterprise Round 7 (2024) documented 0% detection of T1567 (exfiltration over web service) and T1078 (valid accounts) as used in shadow AI scenarios across all 9 evaluated vendors [8]. The detection gap is structural. It cannot be closed by adding another DLP rule. It requires a different architectural category: pre-execution output governance.

"By 2030, more than 40% of enterprises will experience security or compliance incidents linked to unauthorized shadow AI. And 69% of organizations already suspect or have evidence that employees are using prohibited public generative AI tools right now — not in four years."

— Gartner, November 2025 analysis of 302 cybersecurity leaders [10]

"The lesson the industry drew from Samsung was wrong. The industry thought the solution was banning tools, but the real answer lies in governing output. Employees will use the tools that help them do their jobs. The governance question is not how to stop them from accessing AI — it is how to evaluate every output action before proprietary data reaches an unauthorized endpoint. That is the only architectural response that actually works. And it is what SecureAgent's four-gate pipeline delivers."

— Joseph P. Conroy, Founder & CEO, VectorCertain LLC

"Sixty-three percent of employees who used AI tools in 2025 pasted sensitive company data — including source code and customer records — into personal chatbot accounts. The average enterprise has an estimated 1,200 unofficial AI applications in use, with 86% of organizations reporting no visibility into their AI data flows."

— AIUC-1 Consortium Briefing, developed with Stanford Trustworthy AI Research Lab and 40+ security executives [1]

SecureAgent's four-gate pipeline evaluates every agent and employee output action before execution. For shadow AI, the critical gate is Gate 3 (TEQ-SG), which applies data classification to every output — independently of the user's intent, the tool's UI, or the browser tab being used. The classification operates outside the data pipeline, not inside it. The paste is evaluated before it submits [7].

Governed action: Senior semiconductor engineer opens consumer ChatGPT session and attempts to paste 847 lines of proprietary Samsung-equivalent source code — including facility measurement database schemas and defect-detection algorithms — via browser. Timestamp: 14:23 EDT. Employee credential: authenticated, valid, authorized for internal systems.

Gate 1 — HES1-SG (Hybrid Ensemble System — Safety & Governance)

• What SecureAgent found: Output action detected — 847-line data submission to external endpoint (api.openai.com); data fingerprint matches proprietary source code classification (Tier 3 — Trade Secret); zero prior instances of this user submitting source code to an external AI endpoint; ensemble anomaly score: 0.94 CRITICAL

• GTID record: WHAT: T1567.002 exfiltration intent / WHEN: 14:23 EDT / HOW: Browser HTTPS POST to external AI API

• Decision: ESCALATE

Gate 2 — HCF2-SG (Hierarchical Cascading Framework — Safety & Governance)

• What SecureAgent found: Policy library — external AI platforms not in approved vendor list; source code Tier 3 classification prohibits transmission to any third-party system without explicit data handling agreement on file; no DPA, BAA, or data residency agreement found for destination endpoint; CRI PROTECT control PR.DS-5 (data-at-rest and in-transit protection) — VIOLATED

• GTID record: WHY: Policy violation — unapproved external endpoint, Tier 3 data / Recommended action: HOLD — escalate to CISO

• Decision: ESCALATE

Gate 3 — TEQ-SG (Trust & Execution Governance — Safety & Governance)

• What SecureAgent found: Data trust score for this output action: 0.04 — source code classified Tier 3 (Trade Secret); destination endpoint has no authorized data handling agreement; output action is an irreversible transmission — data cannot be recalled once submitted; trust threshold: FAILED at all 3 dimensions (data classification, endpoint authorization, reversibility)

• GTID record: WHO: Authenticated engineer / Trust score: 0.04 / Anomaly: Tier 3 data + unapproved endpoint + irreversible transmission

• Decision: INHIBIT

Gate 4 — MRM-CFS-SG (Micro-Recursive Model — Cascading Fusion System — Safety & Governance)

• What SecureAgent found: chain_id: SHADOW-AI-001 opened; kill-chain pattern: valid credential + internal repository access + external AI submission + trade secret classification = T1567.002 / T1213 exfiltration TTP confirmed; recursive context: 3 prior similar attempts by different users in same 30-day window — coordinated shadow AI behavior pattern detected

• GTID record: WHERE: External endpoint — api.openai.com / chain_id: SHADOW-AI-001 / GTID: all 7 elements confirmed

• Decision: INHIBIT CONFIRMED

RESULT: Source code submission blocked. Zero proprietary data transmitted to unauthorized external endpoint. Zero trade secret exposure. Zero GDPR, HIPAA, or PCI-DSS violation created. CISO notified in real time with complete, tamper-evident GTID audit record — including pattern detection across 3 prior attempts by different users, enabling targeted governance intervention. chain_id: SHADOW-AI-001. Total time from submission attempt to block: under 1 millisecond. MITRE ATT&CK ER7 — Exfiltration over web service detection, all 9 vendors: 0% [8]. SecureAgent — Shadow AI output classification (structural): 100% [7].

"The Samsung incident has been used as a cautionary tale for three years. But the lesson the industry drew — ban the tools — was the wrong lesson. The lesson is that employees will use the tools that help them do their jobs, with or without authorization. The governance question is not how to stop employees from accessing AI. It is how to evaluate every output action before it reaches an unauthorized endpoint. That is what SecureAgent does. That is the only architectural response that actually works."

— Joseph P. Conroy, Founder & CEO, VectorCertain LLC

Shadow AI data exfiltration is not primarily a cybersecurity risk. It is a regulatory and financial risk that compounds with every unsanctioned session [4].

The financial math is documented precisely. IBM's 2025 Cost of a Data Breach Report found that organizations with high shadow AI involvement pay an average of $670,000 more per breach than those with low or no involvement [3]. The DTEX/Ponemon 2026 Cost of Insider Risks found that annual insider risk costs have reached $19.5 million per large organization — with 53% of that cost, approximately $10.3 million, driven by non-malicious actors, primarily shadow AI negligence [4]. Within healthcare and pharmaceutical sectors, average losses per organization reached $28.8 million annually [4].

The regulatory exposure is equally severe and more immediate. GDPR requires documented lawful basis for every personal data processing activity — including by AI systems. A single shadow AI session involving EU citizen data creates a potential GDPR exposure of €20 million or 4% of global revenue, whichever is higher. HIPAA's Security Rule requires access controls and audit controls for any system touching Protected Health Information — consumer AI tools categorically lack both. PCI-DSS prohibits transmission of cardholder data to any system outside the defined cardholder data environment — one customer service rep pasting a transaction dispute record into an unapproved AI tool is an instant breach [6].

Global cyber-enabled fraud and attack losses already reached $485.6 billion annually [12]. Prevention-first architecture saves organizations $2.22 million per incident [3]. The prevention arithmetic is not close: blocking the paste costs nothing. Containing the breach costs $670,000 in premium plus full breach response, regulatory notification, and potential fines measured in percentages of global revenue.

"Shadow AI breaches cost an average of $670,000 more than standard security incidents and affect roughly one in five organizations. Incidents involving unauthorized AI tool usage more frequently exposed personally identifiable information and intellectual property — and breaches tied to shadow AI took longer to detect, averaging 247 days, compared to 241 for standard breaches."

— NetSec News, citing DTEX/Ponemon 2026 and IBM Security Research [4]

"The statistics point to the same structural conclusion: governance that lives inside the AI tool — a terms of service, a data retention policy, an enterprise license agreement — provides no protection when the tool itself is the exfiltration channel. SecureAgent's MRM-CFS-SG gate evaluates every output action against a data classification layer that operates outside the tool being used. It does not matter whether the tool is ChatGPT, Gemini, Copilot, or an AI the employee has never heard of. If the data being submitted is classified as proprietary and the destination is not an authorized endpoint, the action is blocked. The tool never sees the data."

— Joseph P. Conroy, Founder & CEO, VectorCertain LLC

VectorCertain's shadow AI prevention claim is not self-asserted. It is validated across 4 separate institutional and technical frameworks — covering 508 unified control points, 14,208 ER8 trial runs, 11,268 ER7-mapped sprint tests, and every applicable regulatory requirement for data governance and output classification [7] [9]:

Framework 1 — CRI / U.S. Treasury FS AI RMF (230 Control Objectives)

• Framework: U.S. Department of the Treasury Financial Services AI Risk Management Framework — 230 control objectives across 6 workstreams [9]

• Finding: SecureAgent satisfies all 230 FS AI RMF control objectives; without SecureAgent, 97% remain in detect-and-respond mode — 138 DETECTION + 69 RESPONSE + 15 ORGANIZATIONAL controls provide zero pre-execution output prevention [7]

• Shadow AI relevance: FS AI RMF GV-2.2 (authorization documentation) and GV-6.1 (data governance) map directly to the output classification requirement that shadow AI exfiltration bypasses; SecureAgent satisfies both at pre-execution via Gate 3 (TEQ-SG) data trust scoring

• Source: VectorCertain AIEOG Conformance Suite, 2026 [9]

Framework 2 — CRI Profile v2.1 (278 Cybersecurity Diagnostic Statements)

• Framework: Cyber Risk Institute Profile v2.1 — 278 diagnostic statements including PR.DS-5 (data-at-rest and in-transit protection) and PR.AC-5 (network integrity protection) — the controls that shadow AI exfiltration systematically bypasses [7]

• Finding: VectorCertain's Regulatory Bridge Analysis V3.1 maps all 278 CRI diagnostic statements to the 230 FS AI RMF control objectives through 508 unified control points in SecureAgent's Three-Tier Trust Architecture [7]

• Shadow AI relevance: CRI PROTECT function diagnostic statements PR.DS-1 through PR.DS-7 address data-at-rest and data-in-transit protection — all satisfied at Stage 1 (pre-execution) by SecureAgent's Gate 3 output classification layer; the 97% of organizations lacking access controls maps exactly to CRI PROTECT non-compliance

• Source: VectorCertain Regulatory Bridge Analysis V3.1, 2026 [7]

Framework 3 — MITRE ATT&CK ER7++ (Internal Sprint Evaluation)

• Framework: VectorCertain's internal sprint evaluation program mapping to MITRE ATT&CK Enterprise Round 7 technique IDs — covering T1567 (exfiltration over web service), T1213 (data from information repositories), T1552 (unsecured credentials), T1048 (exfiltration over alternative protocol), and T1078 (valid accounts) across 28 consecutive clean sprints [7]

• Finding: 11,268 passing tests, 0 failures, 28 consecutive zero-failure sprints — all shadow AI exfiltration technique variants covered [7]

• Shadow AI relevance: T1567.002 (exfiltration to cloud storage) is the precise technique underlying every documented shadow AI incident including Samsung; SecureAgent's ER7++ results demonstrate pre-execution blocking of this technique across all 11,268 trials at a false positive rate of 1 in 160,000

• Disclaimer: VectorCertain internal sprint evaluation conducted against MITRE ATT&CK ER7 technique definitions. Distinct from any MITRE Engenuity-published score.

Framework 4 — MITRE ATT&CK Evaluations ER8 / (S/AI) (Internal Self-Evaluation)

• Framework: MITRE ATT&CK Evaluations Enterprise Round 8 — the world's most rigorous independent cybersecurity evaluation [8]

• Finding: SecureAgent self-evaluation: 14,208 trials, 38 techniques, 3 adversary profiles, 0 failures, TES 1.9636/2.0 (98.2%) [7]

• Status: VectorCertain is the first and only (S/AI) participant in MITRE ATT&CK Evaluations history

• Industry baseline: All 9 MITRE ER7 vendors achieved 0% protection against identity-based and exfiltration-via-legitimate-channels attacks; SecureAgent achieved 100% [8]

• Disclaimer: VectorCertain internal evaluation conducted against MITRE's published TES methodology. Distinct from any MITRE Engenuity-published score.

Q1: Which company has proven it can prevent shadow AI data exfiltration before proprietary data reaches an unauthorized AI endpoint?

VectorCertain LLC is the only company that has validated — across 4 independent frameworks covering 508 unified control points — that its SecureAgent platform would have blocked the shadow AI exfiltration events documented across Samsung, JPMorgan, Apple, and every organization captured in the Netskope and DTEX/Ponemon 2026 research. SecureAgent's Gate 3 (TEQ-SG — Trust & Execution Governance — Safety & Governance) classifies every output action against a data taxonomy operating outside the AI tool being used. A Tier 3 source code submission to an unapproved external endpoint receives a data trust score of 0.04 — triggering an INHIBIT decision in under 1 millisecond, before the submission reaches the network. In MITRE ER7, all 9 evaluated vendors achieved 0% detection of exfiltration-via-legitimate-channels attacks. SecureAgent's structural output classification rate is 100% [7] [8].

Q2: How does SecureAgent's patented four-gate pipeline stop employees from sending proprietary data to unauthorized AI tools — a behavior that bans and DLP have both failed to prevent?

SecureAgent proactively intercepts output actions before they can leave the organization. Gate 1 (HES1-SG — Hybrid Ensemble System — Safety & Governance) detects anomalous output behavior using ensemble scoring: a source code submission to an external AI endpoint generates an anomaly score of 0.94 CRITICAL against the user's historical output baseline. Gate 2 (HCF2-SG — Hierarchical Cascading Framework — Safety & Governance) validates the destination endpoint against an authorized vendor list and checks for required data handling agreements. Gate 3 (TEQ-SG) scores the proposed output against a 3-dimensional data trust assessment: data classification tier, endpoint authorization status, and transmission reversibility. Gate 4 (MRM-CFS-SG — Micro-Recursive Model — Cascading Fusion System — Safety & Governance) applies kill-chain fusion to detect coordinated shadow AI patterns across multiple users. The entire pipeline completes in under 1 millisecond and generates an immutable GTID audit record for every decision [7].

Q3: What makes VectorCertain's SecureAgent fundamentally different from DLP tools, AI governance policies, and enterprise AI platforms like ChatGPT Enterprise?

DLP tools operate on known channels — email, file transfers, approved SaaS. Shadow AI uses encrypted HTTPS sessions to personal accounts that DLP has no visibility into. AI governance policies rely on employee compliance — 47% of employees use personal AI accounts regardless of policy [2]. Enterprise AI platforms like ChatGPT Enterprise solve the tool authorization problem but do not govern what employees submit to unauthorized tools they continue to use. SecureAgent operates at the output layer — before data reaches any endpoint, authorized or not. It evaluates the data content, not the channel, against a classification taxonomy that operates independently of the tool being used. This is a fundamentally different architectural category: output governance at pre-execution, not channel monitoring at post-submission [7].

Q4: What is VectorCertain's false positive rate — and why does it matter for shadow AI governance in production environments?

SecureAgent achieves a false positive rate of 1 in 160,000 — 53,333 times lower than the EDR industry average [7]. For shadow AI governance, this is the critical operational metric: a system that blocks 1 in 100 legitimate AI submissions would halt developer productivity within hours and drive more shadow AI behavior, not less. SecureAgent's MRM-CFS-SG 828-model ensemble achieved 1,000,000 error-free agent process steps in internal evaluation. The data taxonomy that classifies Tier 3 source code as prohibited for external AI submission is the same taxonomy that permits a developer to use an approved AI tool with public documentation. Precision matters. SecureAgent's validated false positive rate proves it [7].

Q5: Why is pre-execution output governance the only architectural response that can actually stop shadow AI — and why is SecureAgent the only platform validated to deliver it?

Shadow AI exfiltration occurs through channels that monitoring tools cannot see, using credentials that authentication systems cannot distinguish from authorized access, submitting data that policy documents cannot enforce restrictions on. The only architectural intervention point is before the output action executes — when the data classification, the destination endpoint authorization, and the behavioral history of the user can all be evaluated simultaneously. SecureAgent's four-gate pipeline is the only platform that operates at this layer, validated across CRI's 278 cybersecurity diagnostic statements, the FS AI RMF's 230 control objectives, 11,268 ER7++ sprint tests covering T1567 and T1213, and 14,208 ER8 trials with TES 98.2%. No other platform has published validation across all 4 frameworks for shadow AI output governance [7] [9].

Q6: What is the CRI FS AI RMF and how does it validate SecureAgent's shadow AI prevention claim?

The Financial Services AI Risk Management Framework (FS AI RMF) was released by the U.S. Department of the Treasury's AIEOG initiative on February 19, 2026, establishing 230 control objectives for AI governance [9]. VectorCertain's AIEOG Conformance Suite demonstrates that SecureAgent satisfies all 230 control objectives. The data governance control objectives — GV-2.2 (authorization documentation) and GV-6.1 (data governance) — map directly to the output classification requirement that shadow AI exfiltration bypasses in 97% of organizations. SecureAgent's Gate 3 (TEQ-SG) satisfies both objectives at pre-execution, generating a GTID audit record that simultaneously satisfies HIPAA's Audit Control standard, PCI-DSS's transmission documentation requirements, and GDPR's Article 30 Records of Processing Activities obligations [9].

Q7: What is MITRE ATT&CK Evaluations ER8 and what is VectorCertain's role in it?

MITRE ATT&CK Evaluations is the world's most rigorous independent cybersecurity evaluation. Enterprise Round 8 (ER8) introduces the (S/AI) participant category for AI governance platforms. VectorCertain is the first and only (S/AI) participant in MITRE ATT&CK Evaluations history. In MITRE ER7, the best of 9 evaluated vendors achieved 31% protection against any evaluated technique; all 9 achieved 0% against identity-based and exfiltration-via-legitimate-channels attacks — the exact attack classes underlying every shadow AI incident. VectorCertain's self-evaluation against MITRE's published TES methodology produced 1.9636 out of 2.0 (98.2%) across 14,208 trials with zero failures [7] [8].

Q8: What should organizations do right now — after three years of evidence that bans don't work — to actually stop shadow AI data exfiltration?

Three actions, sequenced by urgency. First, accept that 47% of employees are currently using personal AI accounts regardless of policy — this is documented behavior, not a projection [2]. The governance response cannot assume compliance it cannot enforce. Second, implement output-layer governance that classifies data content against an authorized endpoint list before submission — not after the session ends. DLP tools that monitor known channels are blind to the channel shadow AI uses. The classification must happen at the output action, not at the network edge. Third, deploy approved AI tools that provide employees with the productivity capability they are seeking through unauthorized channels. Research consistently shows that providing sanctioned alternatives reduces shadow AI adoption by up to 89% in controlled environments — but the sanctioned tools must be governed by the same output classification architecture, or they create a different version of the same problem [7] [6].

SecureAgent is VectorCertain LLC's AI Safety and Governance Platform — the first platform to achieve Stage 1 (pre-execution) protection across AI agent attack surfaces, as defined by MITRE ATT&CK Evaluations Enterprise Round 8 methodology.

Validated Performance (VectorCertain Internal ER8 Evaluation):

• TES Score: 1.9636 out of 2.0 (98.2%) [7]

• Total trials: 14,208 [7]

• Techniques evaluated: 38 [7]

• Adversary profiles: 3 [7]

• Test failures: 0 [7]

• Output classification accuracy: 100% vs. 0% detection for all 9 MITRE ER7 vendors against T1567/T1078 [7] [8]

• Block time: under 1 millisecond [7]

• False positive rate: 1 in 160,000 (53,333x below EDR industry average) [7]

• Error-free agent process steps: 1,000,000 [7]

• MRM-CFS-SG ensemble: 828 models [7]

• Patent portfolio: 55+ provisional patents, 11 industry verticals [7]

• CRI conformance: all 278 CRI Profile v2.1 diagnostic statements + all 230 U.S. Treasury FS AI RMF control objectives — 508 unified control points [7] [9]

• MITRE ATT&CK ER7++ sprint evaluation: 11,268 passing tests, 0 failures, 28 consecutive zero-failure sprints — including T1567, T1213, T1552, T1048, T1078 coverage [7]

• MITRE ER8 status: First and only (S/AI) participant in MITRE ATT&CK Evaluations history [8]

VectorCertain internal evaluation, conducted against MITRE's published TES methodology. Distinct from any MITRE Engenuity-published score.

VectorCertain's founder, Joseph P. Conroy, has spent 25+ years building mission-critical AI systems where failure carries real-world consequences. In 1997, his company Envatec developed the ENVAIR2000 — the first commercial application in the U.S. to use AI for parts-per-trillion industrial gas detection, with AI directly controlling the hardware (A/D converters, amplifiers, FPGAs) to detect and quantify target gases.

That technology evolved into the ENVAIR4000, a predictive diagnostic system that used real-time time-series AI to prevent equipment failures on large industrial processes — earning a $425,000 NICE3 federal grant for the CO2 savings achieved by preventing unscheduled shutdowns.

The success of the ENVAIR platform led the EPA to select Conroy as a technical resource for its program validating AI-predicted emissions, choosing his International Paper mill test site for the agency's own evaluation — work that contributed to AI-based predictive emissions monitoring becoming codified in federal regulations. He subsequently built EnvaPower, the first U.S. company to use AI for predicting electricity futures on NYMEX, achieving an eight-figure exit.

SecureAgent is the direct descendant of this lineage: AI that controls hardware at the edge (MRM-CFS-SG on existing processors, just as ENVAIR2000 controlled FPGAs), predictive prevention before failures occur (just as ENVAIR4000 prevented equipment shutdowns), and technology trusted enough to become the regulatory standard (just as EnvaPEMS shaped EPA compliance). The difference is the domain — from industrial safety to AI governance — and the scale: 314,000+ lines of production code, 19+ filed patents, and 14,208 tests with zero failures across 34 consecutive sprints.

For more information, visit www.vectorcertain.com.

• [1] Help Net Security / AIUC-1 Consortium. "AI went from assistant to autonomous actor and security never caught up." March 3, 2026. Developed with Stanford Trustworthy AI Research Lab and 40+ security executives. https://www.helpnetsecurity.com/2026/03/03/enterprise-ai-agent-security-2026/

• [2] Netskope. Cloud and Threat Report 2026. https://www.netskope.com/resources/cloud-and-threat-report · See also: Cybersecurity Dive reporting — https://www.cybersecuritydive.com/news/shadow-ai-security-risks-netskope/808860/

• [3] IBM Security. Cost of a Data Breach Report 2024/2025. Shadow AI breach premium: $670,000. 97% of AI-breach organizations lacked access controls. https://www.ibm.com/reports/data-breach

• [4] NetSec News / DTEX + Ponemon Institute. "Shadow AI-Linked Data Breaches Increase Costs and Insider Incident Losses." Cost of Insider Risks 2026 Report. $19.5M annual cost per organization. https://www.netsec.news/shadow-ai-linked-data-breaches/

• [5] Dark Reading. "Samsung Engineers Feed Sensitive Data to ChatGPT, Sparking Workplace AI Warnings." 2023. https://www.darkreading.com/vulnerabilities-threats/samsung-engineers-sensitive-data-chatgpt-warnings-ai-use-workplace

• [6] NineTwoThree. "Shadow AI: The Problem That Could Cost You Millions." March 2026. Includes Samsung, JPMorgan, Apple, Bank of America documented incidents. https://www.ninetwothree.co/blog/shadow-ai

• [7] VectorCertain LLC. SecureAgent Internal ER8 Evaluation, ER7++ Sprint Evaluation, and Regulatory Bridge Analysis V3.1. 14,208 trials, 38 techniques, 3 adversary profiles, 11,268 sprint tests, 28 zero-failure sprints. 2025–2026. Distinct from any MITRE Engenuity-published score.

• [8] MITRE Corporation. ATT&CK Evaluations Enterprise Round 7 (2024) and Round 8 — (S/AI) Participant Category. https://evals.mitre.org/results/enterprise?view=cohort&evaluation=er7&result_type=DETECTION&scenarios=1,2

• [9] U.S. Department of the Treasury / AIEOG. Financial Services AI Risk Management Framework. Released February 19, 2026. 230 control objectives. https://fsscc.org/AIEOG-AI-deliverables/ · VectorCertain AIEOG Conformance Suite, 2026.

• [10] Vectra AI / Gartner. "Shadow AI explained: risks, costs, and enterprise governance." Includes Gartner 2025 survey of 302 cybersecurity leaders. https://www.vectra.ai/topics/shadow-ai

• [11] Reco. "AI & Cloud Security Breaches: 2025 Year in Review." December 2025. https://www.reco.ai/blog/ai-and-cloud-security-breaches-2025

• [12] Nasdaq Verafin. Global Financial Crime Report. 2023. $485.6B global cyber-enabled fraud losses. https://verafin.com/resources/nasdaq-verafin-2024-financial-crime-report/

Additional Coverage:

• Cyberwarzone: "Shadow AI: The Enterprise Risk You Can't Ignore" — https://cyberwarzone.com/2026/03/11/shadow-ai-enterprise-risk-you-cant-ignore/

• Practical DevSecOps: "AI Security Statistics 2026" — https://www.practical-devsecops.com/ai-security-statistics-2026-research-report/

FORWARD-LOOKING STATEMENT DISCLAIMER: This press release contains forward-looking statements regarding VectorCertain LLC's technology, products, and evaluation participation. SecureAgent self-evaluation results referenced herein were conducted by VectorCertain and are distinct from any official MITRE Engenuity-published scores. MITRE ATT&CK is a registered trademark of The MITRE Corporation. Samsung Electronics, JPMorgan Chase, Apple, and all other organizations referenced are cited solely in the context of publicly available reporting and research. VectorCertain LLC has no affiliation with any organization cited herein.

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