Company context
Private, VC-backed
Seed-stage enterprise AI governance and security platform with ISO, SOC 2, HIPAA, and GDPR claims on company page
Vendor research
Singulr 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.
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.
- $10M known funding
- 11-50 employees
- Founded 2023
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 16
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 2
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.
Founded2023
HeadquartersPalo Alto, California; Pune, India office
OwnershipPrivate, VC-backed
Employees11-50
Capital and scaleIndependent company
Singulr AI
- Known funding
- $10M
- Operating scale
- Seed-stage enterprise AI governance and security platform with ISO, SOC 2, HIPAA, and GDPR claims on company page
- Backing context
- $10M seed led by Nexus Venture Partners and Dell Technologies Capital
Seed · $10M · 2025-02-01
Named investors
Nexus Venture Partners · Dell Technologies Capital
Founders and leadership2 people listed
- Shiv AgarwalCurrent role listed
Co-founder & CEO
Previously co-founded Arkin Net (acquired by VMware for $175M in 2016). Led AI/ML product development at VMware. - Abhijit SharmaCurrent role listed
Co-founder & CTO
Previously co-founded Arkin Net. Deep expertise in network security and AI systems.
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
- 2023
- Workforce scale
- 11-50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Company sources and research limits3 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 sourceSingulr company pageSingulr lists offices in Palo Alto and Pune and names Nexus Venture Partners and Dell Technologies Capital among investors.Company sourceFinSMEs funding profileSingulr raised $10M in seed financing led by Nexus Venture Partners and Dell Technologies Capital.Company information sourceStored company websiteSupports 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
- Buyer diligence should validate deployment scale and support maturity because the company is comparatively early-stage.
- Notable for founders with prior enterprise infrastructure exits and a U.S./India operating footprint.
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
- 16
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 16
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 16
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 3
- 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.
- 01Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- 02Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
- 03AI-feature discovery in business applications
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.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 05Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 06Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 07Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 08Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Strong public supportDiscover 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
Strong public supportInventory 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
Strong public supportBlock, 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
Limited public supportDetect, 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
Limited public supportApply 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
Limited public supportDiscover, 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
Strong public supportMediate 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
Strong public supportAuthorize, 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
Limited public supportInventory, 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
Limited public supportRegister 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
Limited public supportDiscover 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
No supporting claim foundVisibility, 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
No supporting claim foundPublicly 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 19 source records. Open additional records only when needed.
Singulr claims policy authoring, ownership, risk thresholds, compliance scoring, lifecycle risk records, dataset licensing validation, agent topology and drift, and closed-loop governance across clouds, software as a service (SaaS), agents, models, data, and tools.
Define enforceable intent, ownership, and risk thresholds across AI systems and agents.
Singulr claims continuous, application-aware AI red teaming against OWASP large language model (LLM), NIST, and MITRE scenarios integrated into CI/CD.
Application-aware AI red teaming runs continuously against OWASP LLM Top 10, NIST, and MITRE scenarios, built into CI/CD.
Singulr claims dataset licensing validation, agent dependency topology, model and tool connection mapping, Model Context Protocol (MCP) configuration risk, Model Context Protocol (MCP) server vulnerability scanning, and continuous model and control drift monitoring.
MCP server vulnerability scanning.
Singulr claims real-time enforcement across AI interactions at browsers, endpoints, and agentic paths, blocking unapproved services, PII or PHI exposure, prompt injection, unauthorized exfiltration, and agent tool or system access.
Real time enforcement across agents and agentic interactions.
Singulr claims agent discovery, ownership and intent, topology and permission mapping, enforceable boundaries based on agent type, data sensitivity, tool access, and scope, plus runtime restriction of unauthorized system access.
Define enforceable policies based on agent type, data sensitivity, tool access, and scope.
Singulr claims browser and endpoint enforcement, agent discovery, Model Context Protocol (MCP) server vulnerability scanning, tool and permission mapping, data-loss prevention, CI/CD red teaming, and runtime controls across agentic execution paths.
Browser and Endpoint Enforcement with Agent Permission Boundaries.
Show 13 additional evidence records
Singulr platform materials reviewed did not establish AI usage-cost attribution, token or spend metrics, budgets, chargeback, or cost-aware model routing.
No quoted source text is recorded for this claim.
Singulr claims Contextual Discovery creates a real-time inventory of the complete AI landscape and continuously discovers homegrown agents, public AI services, and embedded software as a service (SaaS) AI features.
Create real time inventory of your complete AI landscape and eliminate shadow AI. Continuously discover homegrown agents and applications, public AI services, and embedded SaaS features.
Singulr claims it continuously discovers embedded software as a service (SaaS) AI features as part of its complete AI landscape inventory.
Create real time inventory of your complete AI landscape and eliminate shadow AI. Continuously discover homegrown agents and applications, public AI services, and embedded SaaS features.
Singulr claims user activity and data-flow mapping across AI interactions to support visibility into how AI is used.
User activity and data flow mapping
Singulr claims Runtime Control can enforce against unapproved AI services, redact PII and PHI before exposure, control prompt injection, and bound agent permissions.
Enforce against unapproved AI services, redact PII and PHI before exposure, control prompt injection, and bound agent permissions at the browser, the endpoint, and across agentic execution paths.
Singulr claims granular policies can use data classification and include redaction as an enforcement action.
Apply broad governance rules or granular controls based on AI service type, user context, data classification, or business purpose.
Singulr Agent Pulse claims to discover every AI agent and build context graphs of tool connections, data access, Model Context Protocol (MCP) servers, and permissions.
Discover every AI agent in your environment on any platform. We create the context graph of tool connections, data access, MCP servers, and permissions to show how agents interact with your enterprise systems.
Singulr Agent Pulse claims real-time enforcement across agents and agentic interactions to safeguard against unauthorized system/tool access, prompt injections, and data leaks.
Real time enforcement across agents and agentic interactions. Safeguard against unauthorized system and tool access, prompt injections, and data leaks.
Singulr Agent Pulse claims auditable records of agent actions, data access, and policy enforcement, with controls based on agent type, tool access, and scope.
Agent Pulse provides auditable records of agent actions, data access, and policy enforcement
Singulr claims it enforces against unapproved AI services, redacts PII and PHI before exposure, controls prompt injection, and bounds agent permissions at the browser, the endpoint, and across agentic execution paths.
Enforce against unapproved AI services, redact PII and PHI before exposure, control prompt injection, and bound agent permissions at the browser, the endpoint, and across agentic execution paths.
Singulr claims Runtime Control enforces against unapproved AI services, redacts PII and PHI, controls prompt injection, and bounds agent permissions across browser, endpoint, and agentic execution paths.
Enforce against unapproved AI services, redact PII and PHI before exposure, control prompt injection, and bound agent permissions at the browser, the endpoint, and across agentic execution paths. Singulr Agent Pulse™ delivers runtime control for the agentic enterprise: agent discovery, topology mapping, control-drift metrics, and MCP server vulnerability scanning.
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.