Neo Security

1.1 Identity and product thesis

Neo Security presents itself as the Agentic Software Control company, focused on the layer between enterprise software adoption and security enforcement. Its official description says the platform inventories, attributes, and enforces controls across software classes running on managed and unmanaged endpoints. Independent coverage describes the same wedge as a real-time control layer for AI agents, agent-enabled applications, browsers, identities, plugins, MCP servers, and traditional software acquiring autonomous capabilities. This is a specific positioning choice rather than a generic AI-security label: Neo is selling visibility, attribution, and in-line policy enforcement for software acting with legitimate permissions. The central diligence question is whether this control plane becomes a durable product category or a feature absorbed by broader security, identity, or data-governance platforms.[CO001, CO002, CO003, CO004, CO013]

1.2 Founding team and governance visibility

Public materials identify Nicholas Warner as CEO and co-founder, Shlomi Salem as CPO and co-founder, and Eran Shirazi as CTO and co-founder. Their backgrounds are unusually relevant to the proposed product: Warner built SentinelOne's go-to-market organization and helped take it public, Salem led detection engineering and threat research there, and Shirazi combines EasySend founding experience with enterprise research and development and Unit 8200 vulnerability-research experience. The sources support founder-market fit and functional coverage across go-to-market, security research, and engineering. They do not establish a board roster, formal governance controls, succession plan, or the degree to which Neo depends on any one executive, so those remain diligence items rather than conclusions.[CO005, CO006, CO007, CO008, CO009, CO010]

1.3 Financing, footprint, and disclosure

Neo emerged from stealth on July 20, 2026 with $100 million in combined funding. Calcalist and BARC describe the disclosed composition as a $75 million Series A led by Andreessen Horowitz and Bessemer Venture Partners, plus a previously undisclosed $25 million seed completed in 2025; company-issued launch material also names Craft Ventures and Merlin Ventures as participants. Calcalist reports 50 employees, including 40 in Israel, while Dealroom describes a Boston headquarters and Tel Aviv development center. No retained source supplies a post-money valuation, revenue or ARR, customer count, pricing, or named customer references. Funding is therefore evidence of investor commitment, not evidence of cash available after spending, commercial traction, or valuation support.[CO011, CO012, CO013, CO014]

1.4 Milestones and remaining verification

The available record provides a coherent launch sequence: founder experience accumulated before Neo, a 2025 seed round, rapid growth in the agentic-application problem, and the July 2026 stealth launch and Series A. The product announcement describes five capability groups: inventory, capability and risk intelligence, real-time attribution, granular policy control, and native enforcement. BARC's July assessment treats agent governance as a young, funded segment and warns of consolidation and overlap with existing security, identity, and data-governance investments. eWeek adds a material counterweight: Neo has tested with organizations in financial services, transportation, and energy, but has not disclosed their names, pricing, detailed architecture, or independent performance results. The next diligence cycle should prioritize production references, technical validation, customer concentration, board and governance records, and financing terms.[CO004, CO008, CO012, CO013, CO015]

1.5 Exhibits

2.1 Market boundary and buying center

The relevant market is software that identifies, observes, governs, and enforces actions by AI agents and agent-enabled applications inside enterprise workflows. It includes inventory and posture, identity and delegated authority, runtime policy enforcement, approval controls, auditability, and compliance evidence. It excludes general-purpose endpoint, identity, API-gateway, and model-observability spend unless those products are specifically extended to govern autonomous software. The boundary matters because Neo is described as a real-time control layer across agents, applications, browsers, identities, plugins, extensions, MCP servers, and traditional software becoming agentic, while an independent landscape separates Identity & Access, Posture, Runtime Protection, Runtime Authorization, and Compliance. The primary buyer is therefore likely a security organization, with SecOps owning operational control and identity, risk, compliance, and application owners influencing deployment. Adoption is triggered when agents gain write access or cross-application reach; the status quo is inventorying manually, relying on existing permissions, or accepting application-level controls without agent-specific attribution.[CM001, CM002, CM003, CM004]

2.2 Sizing lenses and growth drivers

The most usable sizing lens is a published agentic-AI-security forecast, not a top-down claim that all cybersecurity spend is addressable. MarketsandMarkets projects the category from USD 1.65 billion in 2026 to USD 13.52 billion in 2032 at a 42.0% CAGR, with solutions at 71.32% of 2026 share and North America at 41.92%; those figures are analyst estimates and include a broad ecosystem of threat detection, guardrails, red teaming, identity, and AI workload protection. A second lens is penetration: SiliconANGLE reports Gartner's estimate that only 5% of enterprise applications had agentic features at the end of 2025, versus more than 40% expected by the end of 2026. The growth case is thus driven by expanding agent exposure and the need to connect visibility to action, but the market is still early and the forecast's category perimeter is wider than Neo's endpoint and software-control wedge. NIST and the EU AI Act add governance and traceability pressure, while the absence of binding agent-specific requirements and fragmented standards can slow purchasing and make budgets difficult to assign.[CM005, CM006, CM007, CM008, CM009]

2.3 Adoption constraints and valuation relevance

The buyer problem is urgent but not yet a clean, repeatable budget category. EWeek says organizations must connect each agent to an owner, approved purpose, permission scope, and expiration policy, and reports that Neo has tested its software with unnamed financial-services, transportation, and energy organizations while not disclosing pricing, detailed architecture, or independent performance results. That combination supports a plausible enterprise need but leaves conversion, willingness to pay, and implementation friction unresolved. The product thesis is differentiated from passive monitoring because Neo claims native enforcement, granular controls, and attribution; however, a landscape source says most vendors concentrate on only one or two capability areas, so Neo will face identity, posture, runtime, compliance, and incumbent-security alternatives. EU high-risk obligations emphasize risk assessment, logging, documentation, human oversight, cybersecurity, and accuracy, but their application depends on use case and transition dates. Valuation should therefore credit category timing and control-layer relevance while discounting for unknown pricing, unnamed references, absent independent benchmarks, and uncertain SAM/SOM conversion from the broad forecast.[CM010, CM011, CM012, CM013, CM014]

2.4 Exhibits

3.1 Category landscape and competitive pressure

Neo is positioned as a neutral control layer for agentic software, spanning discovery, attribution, policy and enforcement across endpoints. Its own description says it inventories, attributes and enforces across every software class on every endpoint, while independent coverage describes the product as a real-time layer that inventories, monitors and governs agents, browsers, identities and traditional software. That positioning places Neo between specialized agent-security vendors and platform vendors extending existing endpoint, identity, network and AI-governance products. Security Boulevard reports that the category question is not whether the problem exists but whether agentic security becomes a standalone market or a feature absorbed by larger platforms. Cisco's acquisition of Astrix illustrates the latter path: a large incumbent is adding agent and non-human-identity discovery, lifecycle management and threat response to a broader security stack. The competitive implication is that Neo must win on cross-platform coverage and control quality quickly enough to offset incumbent distribution and bundling.[CP001, CP002, CP003, CP004]

3.2 Capability, deployment and trust comparison

The most material buying distinction is enforcement location and scope. Neo claims endpoint visibility into local agents, MCP servers, extensions and scripts, with attribution and native blocking; its public About page uses the shorter formulation of seeing and controlling every human and non-human action on every endpoint in real time. By contrast, a competitor-authored platform comparison frames agent security around runtime controls, guardrails, observability and testing, and lists Palo Alto, Cisco/Robust Intelligence, Microsoft, open-source frameworks and other vendors across different layers. Cisco describes its own approach as combining identity discovery and management, access enforcement and runtime behavioral protection, with Astrix adding non-human identity and agent capabilities. Curity's vendor-issued architecture reference is not a Neo product source, but it supplies a useful technical baseline: identity, API management, entitlement, machine identity and security operations are separate concerns connected through standards. Against that baseline, Neo's endpoint-first promise could be differentiated, but public evidence does not yet establish breadth across Linux, servers, cloud-hosted agents, integrations or production scale.[CP005, CP006, CP007, CP008]

3.3 Moat durability, pricing and displacement risk

Neo's possible moat is the combination of endpoint telemetry, a continuously enriched software knowledge base, identity attribution and enforcement at the action point. StackScout describes Neoverse as a knowledge base for known agentic software and calls native enforcement a differentiator, but the same review says there are zero public customer references, no third-party validation and no published pricing. These are not minor disclosure gaps for a security control product: they make efficacy, total cost and switching economics hard for a CISO to underwrite. Security Boulevard's historical analogy is adverse to category durability, arguing that network access control functions were absorbed into endpoint, identity and networking platforms, and identity stacks. Neo therefore has a window to become a control-plane platform, but it should be evaluated with a short contract and a proof-of-value against the buyer's own agent inventory until coverage, latency, policy quality and renewal economics are independently demonstrated.[CP009, CP010, CP011, CP012]

3.4 Competitive diligence priorities

The public evidence supports a differentiated thesis, not a proven moat. Buyers should test Neo against platform-bundled alternatives such as Microsoft and Cisco, specialist runtime and identity offerings, and the status quo of existing endpoint, IAM, SIEM and API controls. The comparison should measure discovery completeness, attribution fidelity, policy enforcement, cloud and Linux coverage, deployment effort, latency, false positives, integration burden and the ability to export telemetry and policies if the category consolidates. Pricing remains an explicit unknown, so the assigned evidence does not support a realized price, contract value or valuation input. The central investment risk is not that no competitor exists; it is that a well-funded incumbent can reproduce enough of the control layer, bundle it into an existing contract and win distribution before Neo accumulates customer evidence.[CP013, CP014, CP015, CP016]

3.5 Exhibits

4.1 Revenue model and monetization visibility

Neo describes a platform sold to enterprise security operations teams for inventory, capability and risk intelligence, attribution, policy control, and native enforcement across agentic software. That product description supports a recurring enterprise-software hypothesis, but the public record does not establish whether contracts are priced by endpoint, user, agent, identity, software inventory, usage, or negotiated annual commitment. Neo's official page describes the buyer and control surface but gives no price card, contract terms, customer count, or realized revenue. Independent reporting says the company has tested its software with organizations in financial services, transportation, and energy, while naming none and disclosing neither pricing nor independent performance results. List-like product scope must not be treated as revenue, ARR, or gross margin. The diligence priority is a contract-level bridge from deployed scope to recognized revenue, including implementation, support, renewal, and usage components.[CI001, CI002, CI003, CI004]

4.2 Cost structure and unit-economics gaps

The disclosed operating plan is expansionary: Neo said it would use capital to scale go-to-market and engineering, and coverage describes a 50-person company with 40 employees in Israel. Those signals indicate meaningful forward investment but do not quantify cash burn, gross margin, hosting cost, support burden, sales-cycle length, CAC, payback, or contribution margin. The product's real-time inventory, attribution, policy evaluation, and native enforcement may carry infrastructure, telemetry, integration, and customer-success costs, but the retained sources do not provide measured cost-to-serve. No public evidence establishes that customer revenue offsets this hiring and technical build phase. Underwriting should therefore use an explicit unavailable-data case rather than imputing SaaS benchmarks, and request cohort economics by deployment size and contract vintage.[CI005, CI006, CI007, CI008]

4.3 Capital adequacy and financial verdict

Neo emerged from stealth with reported aggregate funding of $100 million, described by multiple sources as a $75 million Series A plus a previously undisclosed $25 million seed. The exact cash balance, post-money valuation, liquidation preferences, debt, committed facilities, current burn, and runway remain undisclosed. The funding is a meaningful buffer for building a security platform, but raised capital is not cash available: spending since the seed and Series A, working-capital needs, hiring pace, and obligations are unknown. Reporting that the company planned to expand engineering and go-to-market supports a likely financing-dependent growth posture, not a quantified runway. The financial verdict is track/research-more: capital access looks stronger than financial transparency, while the absence of public revenue and unit economics prevents a margin-path or valuation conclusion. A financing model should be refreshed from bank balance, monthly burn, bookings, renewal data, debt schedule, and next-round trigger.[CI009, CI010, CI011, CI012, CI013, CI014]

4.4 Underwriting blockers

The central blocker is not a negative reported financial event; it is incomplete disclosure. Public sources support the funding chronology and product thesis, but do not support a revenue run-rate, ARR, revenue mix, gross margin, customer concentration, pricing realization, cash balance, burn, runway, or debt view. Independent commentary also flags that Neo has not disclosed named customers, pricing, detailed architecture, or independent performance results. Those gaps matter because enterprise security companies can have attractive software-like economics only after implementation complexity, support load, data volume, and renewal behavior are demonstrated. The next diligence package should reconcile capitalization-table funding to cash, show bookings-to-revenue recognition, provide customer-level concentration and retention, and separate one-time services from recurring software revenue.[CI003, CI006, CI010, CI012, CI014]

4.5 Exhibits

5.1 Product definition and customer workflow

Neo positions its product as a real-time control layer for agentic software rather than a conventional API gateway or a report-only discovery tool. Its stated workflow is to reveal what agentic software exists on endpoints, understand what each component can access and how it is configured, attribute activity to a human, agent, or application, and enforce controls over tool calls, API access, and data movement. The official company description says Neo “inventories, attributes, and enforces across every software class your workforce runs in real time on every endpoint, managed or not.” Bessemer describes a related operating loop: observe traffic, propose policy from observed behavior, and let security teams extend policy in plain language. This is a plausible time-to-value mechanism, but it remains a company and investor description rather than independently measured proof of detection quality or policy precision.[CE001, CE002, CE003, CE004]

5.2 Modules and technical architecture

The public product map has five functional layers. Neoverse is described as the inventory layer for AI agents, plugins, extensions, MCP servers, and agentic-enabled applications. Capability and risk intelligence maps access and configuration; real-time attribution creates an audit trail; granular software control applies identity- or group-level policy; and native enforcement blocks risky activity without handing enforcement to another product. The differentiator is the endpoint vantage point: Bessemer says Neo built an actual endpoint sensor because it is the vantage point that can intercept and govern an agent in real time. This could cover local coding assistants and desktop applications that do not traverse a network proxy, but the public record does not disclose sensor privileges, operating-system coverage, telemetry handling, latency, fail-open behavior, or integration APIs. The architecture map therefore represents disclosed functional roles, not a verified implementation diagram.[CE005, CE006, CE007, CE008, CE009]

5.3 Maturity, integrations, and differentiation

Neo has a coherent differentiation thesis: discover agentic software that may not self-report, then govern it at the endpoint rather than relying only on API visibility. Security Boulevard describes the platform as spanning inventory, monitoring, attribution, and control, while Palo Alto's competing Prisma AIRS 3.0 shows that large platform vendors are also assembling discovery, assessment, and runtime control across cloud, SaaS, and endpoint environments. That competitor evidence means Neo's category is strategically important but not uncontested. A separate practitioner-oriented MCP governance source highlights identity federation, action-level RBAC, audit logging, and exportable policies as enterprise gateway requirements; these are relevant diligence questions for Neo, not proof that Neo provides them. The current public record does not establish production integrations, named customers, independent benchmarks, roadmap dates, or public pricing. Enera explicitly says Neo has not published pricing, detailed architecture documentation, or named customer references.[CE010, CE011, CE012, CE013, CE014]

5.4 Trust, safety, and implementation risk

The use case is security-sensitive because agents can inherit valid permissions, call tools, and move through workflows in ways that appear authorized to legacy controls. NIST describes the AI Risk Management Framework as voluntary guidance intended to improve how organizations incorporate trustworthiness into AI design, development, use, and evaluation. IBM's survey of 2,000 technology executives found that 70% said business teams deploy technology faster than IT can track; that figure establishes buyer pain, not Neo product performance. Neo's unresolved implementation questions are consequently material: endpoint deployment and permissions, privacy and data retention, policy correctness, incident response, rollback, coverage across operating systems and SaaS tools, and evidence of reliable enforcement under adversarial or degraded conditions. The next diligence step is a controlled pilot with measurable discovery coverage, attribution accuracy, block/allow precision, latency, and failure-mode behavior.[CE015, CE018]

5.5 Exhibits

6.1 Buyer, user, and payer segments

Neo is positioned for enterprise security and IT teams that need to inventory, attribute, and govern autonomous software. The public record points most directly to SecOps and security architecture users, while regulated financial institutions are a particularly important use case because agent permissions, data access, auditability, and third-party risk matter together. The likely economic buyer is an enterprise security or risk owner, but no public source confirms budget ownership, contract structure, pricing, or whether procurement is direct or partner-led. Customer segmentation is therefore directional rather than a measured revenue mix. Transportation and energy references broaden the vertical hypothesis, but they do not show whether Neo's product is repeatable across procurement environments or merely being evaluated by a small design-partner set.[CU001, CU002, CU003, CU004]

6.2 Adoption trajectory and proof quality

The available adoption signal is an early-stage pilot footprint rather than a disclosed customer base. Toflow reports pilots across financial services, transportation, and energy, while independent coverage describes testing with organizations in those sectors without naming them. Neo's own surface describes a real-time control layer and invites evaluation, but does not publish active-account totals, utilization, deployment counts, or customer outcomes. The evidence supports enterprise interest and evaluation activity; it does not support an ARR, retention, active-customer, or conversion claim. The July 2026 launch coverage is recent relative to this refresh, yet the absence of a denominator still prevents a reliable trajectory or growth rate.[CU005, CU006, CU007, CU008, CU009]

6.3 Named references, retention, and expansion

No assigned source names a Neo customer or establishes that a reported pilot reached production. The customer-proof record consequently has three sector-level entries with unknown customer identity, deployment stage, and outcome. Public materials also provide no NRR, GRR, churn, renewal, contract-length, cohort, satisfaction, or concentration data. Neo's proposed workflow could support land-and-expand from inventory into attribution and policy enforcement, but that is a product hypothesis, not observed expansion. Buyers and investors should require reference calls, production telemetry, renewal evidence, and concentration disclosure before underwriting durability. This is the key distinction between a plausible security workflow and validated commercial durability: the former is described, while the latter remains private evidence.[CU010, CU011, CU012, CU013, CU014, CU015]

6.4 Exhibits

7.1 Regulatory and legal exposure

Neo's product sits at the intersection of AI governance, cybersecurity, identity, and enterprise software procurement. The EU AI Act uses risk-based categories and assigns obligations across providers, deployers, importers, and distributors; its enforcement is hybrid, with national authorities handling many system-level duties and the European Commission supervising GPAI rules. That creates a classification and allocation risk for a control layer operating across agents, applications, plugins, and model-connected workflows. Public guidance also emphasizes that applicable duties vary by intended purpose, system role, deployment context, and the specific regulatory pathway. Neo's launch materials describe inventory, attribution, policy control, and native enforcement, but do not disclose its own conformity assessments, certifications, legal opinions, or customer-specific compliance boundaries. A separate 2020 article uses the phrase "Neo-Security Architecture" for Curity's unrelated identity, API, and entitlement design, so researchers must also guard against search-result attribution errors. The investment implication is not that non-compliance exists; it is that enterprise sales may require auditable classification, logging, oversight, incident reporting, contractual allocation, and evidence that are not publicly demonstrated.[CR001, CR002, CR003, CR004, CR015]

7.2 Operational, technical, and security risk

The strongest external risk evidence concerns the environment Neo is selling into rather than a recorded Neo incident. Joint Five Eyes guidance identifies privilege, design and configuration, behavioral, structural, and accountability risks in agentic systems, including prompt injection, excessive access, cascading failures, and difficulty reconstructing why an action occurred. It recommends least privilege, defense in depth, secure identities, staged deployment, human checkpoints, and monitoring of internal action chains rather than only inputs and outputs. Cloud Security Alliance analysis similarly describes prompt injection as persistent and difficult to fix and notes that conventional SIEM tooling may not capture probabilistic multi-step behavior at the required granularity. Neo claims real-time attribution and native policy enforcement, which could be strategically relevant, but the public record does not establish detection efficacy, false-positive rates, rollback performance, coverage of unmanaged endpoints, or resilience under adversarial testing. These gaps leave product liability, trust, and execution exposure until independently testable controls are available.[CR005, CR006, CR007, CR008]

7.3 Partner, dependency, and financial-model risk

Neo emerged from stealth with $100 million from Andreessen Horowitz and Bessemer Venture Partners, with participation from Craft Ventures and Merlin Ventures, and said it would use the capital to scale go-to-market and engineering. The announcement provides funding context but no valuation, revenue, ARR, burn, runway, gross margin, customer count, or renewal data. That makes capital sufficiency and commercial efficiency impossible to underwrite from the assigned public evidence. The product also depends on a changing ecosystem of AI agents, browsers, SaaS platforms, identities, plugins, MCP servers, endpoints, model providers, and enterprise security workflows. External guidance stresses that interconnected tools and multi-agent pipelines expand attack surfaces and can propagate a compromised component downstream. Neo's control-plane thesis therefore carries integration, platform-change, supplier/model, and adoption-timing risk; the company must prove that inventory and enforcement remain complete as counterparties change capabilities, permissions, APIs, and logging behavior.[CR009, CR010, CR011, CR012]

7.4 Mitigations, monitoring, and thesis-break triggers

The public mitigation signal is directionally coherent but not yet company-specific proof. Government guidance recommends starting with low-risk tasks, limiting agent access, using temporary credentials, adding human approval for high-impact actions, and integrating agent governance into existing zero-trust, defense-in-depth, and incident-response structures. EU compliance guidance likewise points to continuous risk management, current technical documentation, logging, post-market monitoring, incident procedures, and clear provider-deployer responsibility. For Neo, diligence should convert these principles into measurable operating tests: inventory coverage across managed and unmanaged endpoints; percentage of actions attributed to a human, agent, application, and identity; policy decision latency; blocked high-risk action recall and false-positive rates; log completeness and retention; rollback time; unresolved critical vulnerabilities; and customer renewal or expansion. A thesis break would be a material uncontained incident, inability to reconstruct consequential actions, repeated control bypasses, a regulatory classification that materially expands obligations without a delivery plan, or evidence that deployment requires broader privileges than customers will accept.[CR013, CR014]

7.5 Exhibits

8.1 Recommendation and decision posture

Neo merits a track/research-more recommendation rather than a priced investment decision. The strongest positive evidence is the verified $100 million financing, including an independently reported $75 million Series A following a $25 million seed round, and a product thesis aimed at inventorying and controlling agentic software at the endpoint. Those facts establish investor sponsorship and strategic ambition, not commercial validation. The retained public evidence still omits the company's post-money valuation, revenue or ARR, retention, pricing, named deployments, and independent performance results. Without those inputs, a precise entry price or target return would be false precision. Risk is high because execution proof is thin while capital and valuation enthusiasm across cybersecurity and AI-security markets remains elevated.[CV001, CV002, CV003, CV011, CV012, CV013, CV014, CV016]

8.2 Thesis, anti-thesis, and financing context

The thesis is that enterprises need a control plane that can discover agentic software, attribute actions, evaluate configuration risk, and enforce policy before sensitive systems are touched. Neo also has founders with relevant enterprise-security operating backgrounds and enough disclosed capital to pursue engineering and go-to-market expansion. The anti-thesis is equally material: agentic security is already crowded, incumbents and venture-backed specialists span identity, gateways, runtime, and governance, and Neo has not publicly shown customer identities, pricing, detailed architecture, or independent results. Financing composition is clearer than valuation: third-party reporting identifies a $75 million Series A and prior $25 million seed, but no retained source provides post-money value, preferences, ownership, debt, or runway.[CV001, CV002, CV003, CV004, CV005, CV011, CV012]

8.3 Scenario framework and entry discipline

Scenario analysis should be milestone-based rather than presented as an absolute dollar valuation. Public cybersecurity benchmarks range widely by business quality and subsector: one retained market source places the general public cyber index at 7.8 times next-twelve-month revenue, security operations at 7.2 times, identity at 10.5 times, and cloud security at 13.9 times, while lower-middle-market M&A averages are much lower. These are market reference points, not Neo valuation evidence. A bull case requires verified recurring revenue, durable retention, referenceable deployments, and technical efficacy. A base case preserves optionality but delays pricing. A bear case follows if deployments remain undisclosed, product claims lack independent validation, or financing terms embed a price that assumes category leadership before commercial proof.[CV006, CV007, CV008, CV009, CV010, CV011, CV013, CV015, CV016]

8.4 Exit readiness, diligence asks, and thesis-break triggers

Neo is not publicly exit-ready on the available evidence. Cybersecurity M&A and funding activity provide a credible strategic-exit backdrop, but transactions reward scarce scaled assets, growth quality, and demonstrable integration value; category activity alone does not establish an exit value for Neo. The investment case should be reopened only after management supplies the cap table and preference stack, ARR and revenue quality, gross and net retention, gross margin, burn and runway, pricing, pipeline conversion, named reference customers, deployment scale, and third-party efficacy tests. Thesis-break triggers include inability to convert pilots into paid production deployments, weak retention, material sensor or enforcement performance shortfalls, excessive preference overhang, or a proposed entry valuation that prices Neo near premium public subsectors without comparable revenue quality and proof.[CV005, CV006, CV007, CV008, CV009, CV011, CV012, CV013, CV014, CV015, CV016]

8.5 Exhibits