AI API and web application protectionAdditional if: System uses APIs, tool servers, or web apps · The system exposes or uses APIs, model endpoints, tool servers, web applications, or agent-to-tool traffic that application or API security controls can inspect.Action-taking agent safeguardsDirectly addresses · Observe and govern agent plans, memory, tool use, delegated tasks, and actions while the agent runs.AI gateway and tool-connection controlsAdditional if: Model or tool traffic uses a security gateway · Model, tool, connector, API, or Model Context Protocol (MCP) traffic is routed through a security gateway.Coding-agent and developer workstation securityAdditional if: Coding agents reach developer systems · Coding agents can reach code editors, command-line tools, repositories, build systems, software packages, or developer workstations.AI data protectionAdditional if: Sensitive content enters AI flows · Sensitive content must be inspected or blocked in prompts, responses, files, retrieved information, or tool calls.AI application runtime protectionDirectly addresses · Protect custom AI applications, information-retrieval systems, model calls, prompts, and outputs while they run.
Vendor research
Akto
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
Related research availableBack 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.
- $5M known funding
- <50 employees
- Founded 2023
Company context
Private independent company; reviewed Akto-controlled sources do not identify an acquirer or parent company
Akto says Fortune 1000 security teams use its platform and that more than one million AI agent-to-tool actions have been analyzed and secured
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 15
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 4
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 Akto-controlled company pages
HeadquartersSan Francisco, California
OwnershipPrivate independent company; reviewed Akto-controlled sources do not identify an acquirer or parent company
Employees<50
Capital and scaleIndependent company
Akto
- Known funding
- $5M
- Operating scale
- Akto says Fortune 1000 security teams use its platform and that more than one million AI agent-to-tool actions have been analyzed and secured
- Backing context
- Akto announced $4.5 million in seed funding led by Accel, with angel investors including Akshay Kothari, Renaud Deraison, and Milin Desai
Akto announced $4.5 million in seed funding led by Accel, with angel investors including Akshay Kothari, Renaud Deraison, and Milin Desai
Founders and leadership2 people listed
- Ankita GuptaCurrent role listed
Co-Founder & CEO
- Ankush JainCurrent 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 Akto-controlled company 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 sourceAkto pricing pageAkto describes workstation and endpoint discovery, governance, guardrails, connectors, agent and MCP mapping, and continuous red teaming.Company sourceAkto about pageAkto identifies its founders, enterprise market focus, investors and advisors, and aggregate customer scale indicators.Company sourceAkto seed announcementAkto's seed announcement names the round size, lead investor, and participating angels.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.
AI API and web application protectionCore product focusAction-taking agent safeguardsCore product focusAI gateway and tool-connection controlsCore product focusCoding-agent and developer workstation securityCore product focusAI testing and adversarial assuranceCore product focusEmployee AI access and usage controlsRelated coverageAI data protectionRelated coverageAI application runtime protectionRelated coverageAI governance, risk, and complianceRelated coverageAI asset and configuration securityRelated coverageEndpoint AI application controlsRelated coverage
Buyer context
- Treat Akto as an agentic AI, endpoint AI-use, Model Context Protocol (MCP), and application programming interface (API) security control plane with both employee-use and application-runtime coverage.
- Public evidence supports workstation and endpoint AI discovery, access governance, prompt and skill-call audit, data guardrails, coding-assistant visibility, agent/Model Context Protocol (MCP) mapping, continuous testing, and Model Context Protocol (MCP) monitoring.
- Public pages reviewed did not establish software as a service (SaaS) embedded-feature inventory, generic non-human identity (NHI) credential lifecycle, or AI spend attribution and budget governance.
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 referenceCSA AI Controls Matrix
- Requirements with public support
- 14
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 13
- 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
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
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
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
Strong public supportPublicly 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.
Akto claims discovery of web and local AI usage across workstations and endpoints, including agents, coding assistants, Model Context Protocol (MCP) servers, and skills.
Discover web & local AI usage - AI Agents, CLI based coding assistant, MCP servers, agent skills & more
Akto 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.
Akto claims context maps that connect each agent to its Model Context Protocol (MCP) servers, tools, and databases.
Context-maps each agent to the MCP servers, tools and databases it connects to
Akto claims user-, team-, and tool-level AI access governance with inline block, redact, or warn actions.
AI access governance by user, team and tool
Akto claims controls that stop PII, secrets, and source code from leaking into AI prompts.
Stops PII, secrets and source code leaking into prompts sent to AI tools
Akto claims endpoint coverage for ChatGPT and other web assistants with real-time block, redact, or warn enforcement.
Covers Claude (web, CLI, desktop, Cowork), ChatGPT and other major AI assistants
Show 13 additional evidence records
Akto claims real-time AI guardrails across employee endpoints and agent-to-tool runtime paths.
Inline policy enforcement wherever AI is used - block, redact or warn in real time
Akto claims forensic audit trails for AI prompts, responses, violations, and skill calls mapped to major AI frameworks.
Logs every prompt, response, violation and skill call as a forensic audit trail
Akto claims continuous red teaming across AI agents and Model Context Protocol (MCP)-connected systems.
runs continuous red teaming
Akto identifies model poisoning, Model Context Protocol (MCP) tool poisoning, tool shadowing, rug pulls, and agent skills within its security scope.
Model Poisoning
Akto claims guardrails between agents and invoked tools that inspect Model Context Protocol (MCP) calls and enforce policy in real time.
sit between your agents and the tools they invoke, enforcing enterprise policies in real time
Akto claims analysis of every Model Context Protocol (MCP) call, tool use, execution context, response structure, and parameter pattern.
analyzes every MCP call, tool usage, execution context, response structure, and parameter pattern
Akto materials reviewed did not provide a public claim for authenticating, authorizing, or enforcing policy over direct agent-to-agent communication.
No quoted source text is recorded for this claim.
Akto materials reviewed did not provide a public claim for generic service-account discovery, application programming interface (API)-key lifecycle, secret rotation, or machine-credential governance.
No quoted source text is recorded for this claim.
Akto claims access governance by user, team, and tool plus actor-aware monitoring for agent and Model Context Protocol (MCP) actions.
AI access governance by user, team and tool
Akto claims discovery and endpoint guardrails for CLI coding assistants, agent skills, source code, ChatGPT, Claude, and Codex use.
CLI based coding assistant, MCP servers, agent skills
Akto 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.
Akto states that its AI security pricing is usage based and enterprise oriented, with sales-assisted packaging.
usage based Enterprise-grade pricing
Akto claims MDM deployment, enterprise AI compliance connectors, and SIEM feeds.
Deploy Akto Endpoint Shield via MDM tools such as Intune, NinjaOne, Automox and more