No solution approach in the current research connects this vendor to this use case.
Vendor research
Backslash Agentic AI Endpoint Security
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.
- $13.5M known funding
- <50 employees
- Founded 2022
Company context
Private independent company; reviewed Backslash-controlled sources do not identify an acquirer or parent company
Backslash names support for major coding and workforce agents including Claude Code, Cursor, GitHub Copilot, Windsurf, Gemini CLI, Codex, Kiro, and OpenClaw
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 13
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 6
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.
FoundedFounding year not stated on the reviewed Backslash-controlled pages
HeadquartersHeadquarters not stated on the reviewed Backslash-controlled pages
OwnershipPrivate independent company; reviewed Backslash-controlled sources do not identify an acquirer or parent company
Employees<50
Capital and scaleIndependent company
Backslash Security
- Known funding
- $13.5M
- Operating scale
- Backslash names support for major coding and workforce agents including Claude Code, Cursor, GitHub Copilot, Windsurf, Gemini CLI, Codex, Kiro, and OpenClaw
- Backing context
- Backslash lists StageOne Ventures and First Rays Venture Partners as investors
Series A · $19M
Founders and leadership2 people listed
- Shahar ManCurrent role listed
Co-Founder & CEO
- Yossi PikCurrent role listed
Co-Founder & CTO
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
- Founding year not stated on the reviewed Backslash-controlled pages
- Workforce scale
- <50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- A current hiring source is not available, so the count is not shown.
Company sources and research limits4 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 sourceBackslash agentic endpoint security pageBackslash describes visibility, governance, runtime protection, MCP and skill controls, coding-agent coverage, and endpoint audit.Company sourceBackslash about pageBackslash describes its mission, founders, investors, and focus on the agentic AI endpoint fabric.Company information sourcecompany_intelSupports the company facts shown in this profile.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.
Coding-agent and developer workstation securityCore product focusEndpoint AI application controlsCore product focusAI gateway and tool-connection controlsCore product focusAction-taking agent safeguardsCore product focusAI application runtime protectionRelated coverageEmployee AI access and usage controlsRelated coverageAI data protectionRelated coverageAI asset and configuration securityRelated coverageAI governance, risk, and complianceRelated coverage
Buyer context
- Treat Backslash as a purpose-built agentic endpoint and AI coding-workstation security platform, distinct from code-only AppSec and network AI gateways.
- Public evidence supports endpoint discovery of agents, Model Context Protocol (MCP) servers, skills, hooks, plugins, and models; centralized policy; Model Context Protocol (MCP) and skill vetting; file and network action monitoring; prompt and exfiltration prevention; and forensic audit.
- Public pages reviewed did not establish software as a service (SaaS) embedded-AI inventory, browser-session controls, direct agent-to-agent (A2A) authorization, generic machine-credential lifecycle, AI FinOps, or a public licensing unit.
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
- 12
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 12
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 12
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 12
- 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.
- 03Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 04Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 05Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 06Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 07Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 08Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
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
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
Strong 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
Strong 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
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
No supporting claim foundTest 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
Strong 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
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
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
Strong 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.
Backslash claims endpoint inventory of unauthorized AI agents, Model Context Protocol (MCP) servers, skills, rules, hooks, plugins, and personal-account installations.
including the ones installed under personal accounts and never reported to IT
Backslash materials reviewed did not provide a public claim for tenant-level inventory of embedded AI features across enterprise software as a service (SaaS) applications.
No quoted source text is recorded for this claim.
Backslash claims end-to-end inventory of agents, MCPs, skills, hooks, plugins, large language models (LLMs), permissions, and workflows across enterprise endpoints.
from AI agents to MCPs, Skills, Hooks, LLMs used and Plug-ins
Backslash claims centralized policies restricting unauthorized models, private-account use, unsafe configurations, and untrusted components.
restrict unapproved models, private account use, and unsafe configurations
Backslash claims real-time prevention of data exfiltration from agentic endpoints, including source code, secrets, credentials, and internal IP.
Detect and prevent attempted data exfiltration
Backslash materials reviewed did not provide a public claim for browser session controls over AI upload, download, copy, paste, sharing, or form submission.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Backslash claims real-time endpoint detection and prevention of prompt injection, data exfiltration, privilege escalation, tool poisoning, and anomalous agent behavior.
detect and prevent data leakage, prompt injections, privilege escalations and drift
Backslash claims centralized agentic endpoint policy plus forensic audit of prompts, Model Context Protocol (MCP) communications, network access, file access, and violations.
audit trail of harness-layer events
Backslash materials reviewed did not provide a public product claim for adversarial AI red teaming, evaluation campaigns, or regression release gates.
No quoted source text is recorded for this claim.
Backslash claims security posture and supply-chain risk scoring for Model Context Protocol (MCP) servers, skills, and plugins with allowlist, blocklist, approval, malware, vulnerability, provenance, and drift controls.
supply-chain risk
Backslash claims an endpoint Model Context Protocol (MCP) proxy intercepting inbound and outbound activity to block data leakage and prompt injection in real time.
MCP Proxy that intercepts both inbound and outbound activities in real time
Backslash claims real-time audit and monitoring of prompts, tool calls, Model Context Protocol (MCP) communications, agent network access, file access, permissions, and actions.
agent network and file access
Backslash materials reviewed did not provide a public claim for authenticating or authorizing direct agent-to-agent communication.
No quoted source text is recorded for this claim.
Backslash claims visibility into agents using environment credentials, embedded permissions, application programming interface (API)-connected tools, and human host identity.
environment credentials
Backslash claims contextual permission policy over agents, tools, models, MCPs, skills, and the human identity used on the host endpoint.
using the human user’s identity on the host machine
Backslash claims direct governance of coding agents, MCPs, skills, hooks, plugins, file and network access, permissions, source data, and endpoint actions.
AI agent and IDE hardening
Backslash materials reviewed did not provide a public claim for AI spend attribution, budgets, chargeback, rate limits, or token-cost anomaly detection.
No quoted source text is recorded for this claim.
Backslash materials reviewed did not provide a public per-user, per-device, per-agent, or platform licensing unit.
No quoted source text is recorded for this claim.
Backslash claims SIEM and SOC integration plus coverage across major coding agents, integrated development environments (IDEs), developer workstations, citizen-developer endpoints, MCPs, and enterprise tools.
integrated into SIEM and SOC tools