No solution approach in the current research connects this vendor to this use case.
Vendor research
LatticeFlow AI
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Use-case context
How this vendor relates to the selected use case
No related approach foundBack to use case →
These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.
Company scale
Emerging?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.This is a company-scale signal, not a product-quality rating.
?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.This is a company-scale signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- $12M known funding
- Founded 2020
- Employee scale not sourced
- Private-company revenue and profitability not sourced
Company context
Private independent company
Technical AI risk platform originating from ETH Zurich research, expanded through the 2026 AI Sonar acquisition, an SAP partnership, and published customer stories including Julius Baer and Axpo
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 16 records
- Source-checked records
- 5
- Evaluation requirements
- 16 in this research model
- Unresolved requirements
- 13
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Founded2020
HeadquartersZurich, Switzerland
OwnershipPrivate independent company
EmployeesNot yet sourced
Capital and scaleIndependent company
LatticeFlow AI
- Known funding
- $12M
- Operating scale
- Technical AI risk platform originating from ETH Zurich research, expanded through the 2026 AI Sonar acquisition, an SAP partnership, and published customer stories including Julius Baer and Axpo
- Backing context
- $12M in known financing in the reviewed company snapshot; LatticeFlow also received an approximately $3M Innosuisse project award in 2024, which is reported separately to avoid double counting
Financing round · $12M
Named investors
Atlantic Bridge · OpenOcean
Founders and leadership4 people listed
- Petar TsankovCurrent role listed
Co-Founder & CEO
- Pavol BielikCurrent role listed
Co-Founder & CTO
- Martin VechevCurrent role listed
Co-Founder
- Andreas KrauseCurrent role listed
Co-Founder & Scientific Advisor
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- 2020
- Workforce scale
- Not yet sourced
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- Current employee range is not yet supported by a public source.
Company sources and research limits7 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourceLatticeFlow AI platformLatticeFlow describes discovery, evaluations, security testing, governance interpretation, agent assessment, and production monitoring.Company sourceLatticeFlow AI Sonar acquisitionLatticeFlow announced its acquisition of AI Sonar to add on-premise AI discovery to its evaluation and governance platform.Company sourceLatticeFlow customer storiesLatticeFlow publishes customer stories involving PastaHR, Unique AI, Julius Baer, and Axpo.Company sourceLatticeFlow Innosuisse announcementLatticeFlow announced an approximately $3M Innosuisse project award to connect AI governance requirements with technical evaluations.Company information sourceLatticeFlow company and founders pageSupports the company facts shown in this profile.Company information sourceLatticeFlow financing and company milestoneSupports the company facts shown in this profile.Company information sourceIAPP AI Governance Vendor Report 2026Supports the company facts shown in this profile.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat LatticeFlow AI as the technical evidence and assurance layer in the governance cohort.
- Published customer and SAP partnership evidence strengthens its market signal, but the reviewed sources do not establish revenue, profitability, or enough capital and operating scale for default enterprise procurement.
- Public evidence covers discovery, use-case evaluations, adversarial testing, framework mapping, agent risk assessment, production monitoring, and audit-ready evidence.
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCIS Critical Security Controls
- Requirements with public support
- 5
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 5
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 5
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 2
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 02Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 03Generative AI application security
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
- 04Generative AI application security
A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
- 05AI governance, risk, and compliance
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
- 06AI governance, risk, and compliance
A policy, assessment, approval, exception, or remediation workflow changes the governed state of the test system.
- 07AI assurance and adversarial testing
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
- 08AI assurance and adversarial testing
Results include reproducible prompts or attack steps, affected component, severity, and remediation guidance.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
No supporting claim foundDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
No supporting claim foundInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
Limited public supportMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
No supporting claim foundBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
No supporting claim foundDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
No supporting claim foundApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
Strong public supportProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
Strong public supportInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
Strong public supportTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
No supporting claim foundDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
No supporting claim foundMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Strong public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
No supporting claim foundAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
No supporting claim foundInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
No supporting claim foundRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
No supporting claim foundDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
Research incompleteVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
Research incompletePublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 16 source records. Open additional records only when needed.
LatticeFlow AI materials reviewed did not provide a public claim for enterprise-wide discovery of unapproved AI use.
No quoted source text is recorded for this claim.
LatticeFlow AI materials reviewed did not provide a public claim for embedded AI discovery across the business-application environment.
No quoted source text is recorded for this claim.
LatticeFlow AI claims continuous discovery of AI endpoints, data sources, and connected components across cloud and on-premise environments.
Continuous Discovery across your AI Ecosystem
LatticeFlow AI materials reviewed did not provide a public claim for policy enforcement over unapproved AI use.
No quoted source text is recorded for this claim.
LatticeFlow AI materials reviewed did not provide a public claim for inspection or enforcement over sensitive data moving through AI.
No quoted source text is recorded for this claim.
LatticeFlow AI materials reviewed did not provide a public claim for browser or software as a service (SaaS)-session controls for employee AI use.
No quoted source text is recorded for this claim.
Show 10 additional evidence records
LatticeFlow AI claims detection and monitoring of security vulnerabilities, adversarial exploits, data leakage, and other AI risks.
security vulnerabilities and adversarial exploits to hallucinations, bias, data leakage, and compliance gaps
LatticeFlow AI claims executable technical controls, continuous risk tracking, framework mappings, and audit-ready evidence.
implement executable technical controls that track AI risk continuously
LatticeFlow AI claims app-specific evaluations and adversarial testing before launch with continuous monitoring afterward.
combining app-specific evaluations and adversarial testing before launch with continuous production monitoring afterward
LatticeFlow AI materials reviewed did not provide a public claim for model and AI component supply-chain inspection.
No quoted source text is recorded for this claim.
LatticeFlow AI materials reviewed did not provide a public claim for inline model, agent, tool, application programming interface (API), or Model Context Protocol (MCP) policy enforcement.
No quoted source text is recorded for this claim.
LatticeFlow AI claims evaluation of agent workflows, security controls, tool permissions, reliability, compliance risk, and multi-step behavior.
assessing security controls, tool access permissions, reliability, and compliance risk across multi-step agent behaviors
LatticeFlow AI materials reviewed did not provide a public claim for trust or policy enforcement between agents.
No quoted source text is recorded for this claim.
LatticeFlow AI materials reviewed did not provide a public claim for non-human identity, service-account, secret, or workload credential lifecycle controls.
No quoted source text is recorded for this claim.
LatticeFlow AI materials reviewed did not provide a public claim for agent registration, delegated authorization, task-scoped access, and revocation.
No quoted source text is recorded for this claim.
LatticeFlow AI materials reviewed did not provide a public claim for coding-agent, integrated development environment (IDE), CLI, workstation, skill, hook, or package-action governance.
No quoted source text is recorded for this claim.