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    Security Operations (SecOps)Startup$100M from stealth

    Neo Security

    Agentic Software Control layer giving SecOps inventory, attribution and policy enforcement over AI agents and apps

    Mkt Cap / ValPrivate
    RevenueEarly Stage
    Jul 2026: Emerged from stealth with $100M ($75M Series A led by a16z and Bessemer)
    SentinelOne-pedigree team with $100M backing building the control plane for agentic software, not just AI app discovery
    Analyst take · Competitive edge

    SWOT Analysis

    Strengths
    • Founders led SentinelOne go-to-market and detection engineering
    • $100M raised at launch from a16z, Bessemer, Craft and Merlin
    • Continuous inventory of agents, models, extensions and MCP servers
    • Real-time attribution ties every software action to a user or app
    • Policy engine can pause tool calls, data movement and API access
    Opportunities
    • Gartner: agentic share of enterprise apps 5% in 2025 to 40% in 2026
    • MCP server and browser-agent sprawl creating urgent governance needs
    • Land with SecOps, expand into identity and data governance
    • Channel leverage via founders' SentinelOne partner relationships
    Weaknesses
    • Just out of stealth; no public customers or case studies yet
    • Product breadth still maturing versus established AI-SPM vendors
    • Agentic software control is an unproven budget line for CISOs
    • Boston and Tel Aviv team must scale sales from a standing start
    Threats
    • Wiz, Palo Alto and CrowdStrike adding AI agent security natively
    • Funded rivals Noma, Zenity and Astrix already selling AI security
    • Model and agent platforms shipping their own guardrails
    • Hype-cycle fatigue if agentic risk does not materialize quickly

    User Sentiment

    Synthesized from G2, Gartner Peer Insights, and analyst review data.

    What users love
    • Unified view of agents, extensions and AI apps across the estate
    • Fast time to first inventory with agentless discovery
    • Attribution detail simplifies investigating autonomous actions
    Common complaints
    • Early-stage product with integrations still being built out
    • Limited public documentation and community knowledge base
    • Pricing and packaging not yet publicly defined

    Customer Profile

    Who buys this

    Typical segments

    Large enterprises deploying AI agentsSecurity-first tech and financial firms

    Typical buyer

    CISO or SecOps lead governing AI adoption

    Top use cases
    1. 1Discovering shadow AI agents and MCP servers
    2. 2Enforcing least privilege for agent tool calls
    3. 3Attributing autonomous actions during incidents

    Future Focus Areas

    1

    Runtime policy enforcement for agent-to-agent traffic

    2

    Browser and identity-layer agentic controls

    3

    Integrations with SIEM, XDR and identity providers

    4

    Agentic software risk scoring and benchmarks