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.AI application runtime protectionDirectly addresses · Protect custom AI applications, information-retrieval systems, model calls, prompts, and outputs while they run.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.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.
Vendor research
Akamai API 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
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
Established?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity signal, not a product-quality rating.
?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity 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.
- $4.2B annual revenue (2025-12-31)
- 11,400+ employees
- Founded 1998
Company context
Public company (Nasdaq: AKAM)
Akamai reports $4.21B in 2025 annual revenue and more than 11,400 employees worldwide.
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 5
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 14
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.
Founded1998
HeadquartersCambridge, Massachusetts
OwnershipPublic company (Nasdaq: AKAM)
Employees11,400+
Capital and scaleOwned business
Akamai Technologies, Inc.
- Latest annual company revenue
- $4.2B
- Current owner annual revenue
- $4.2B
- Operating scale
- Akamai reports $4.21B in 2025 annual revenue and more than 11,400 employees worldwide.
- Backing context
- Akamai public-company platform investment; Akamai completed the approximately $450M acquisition of Noname Security in June 2024.
Akamai Technologies, Inc. · period ended 2025-12-31 · filed 2026-02-20
Akamai Technologies, Inc. (AKAM) · period ended 2025-12-31 · filed 2026-02-20
Founders and leadershipBackground context
Current leadership and public filings provide more useful context for this company than historical founder information.
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
- 1998
- Workforce scale
- 11,400+
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Company sources and research limits6 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 sourceAkamai API Security product pageAkamai describes API Security as continuously discovering and protecting APIs connected to GenAI models, LLMs, AI services, and MCP servers across distributed environments.Company sourceAkamai Firewall for AI product pageAkamai positions Firewall for AI as a separately named product for real-time protection of AI applications and LLM interactions, including prompt-injection blocking, output filtering, and sensitive-data protection.Company sourceAkamai investor-relations acquisition announcementAkamai announced that it completed the Noname Security acquisition on June 25, 2024 for approximately $450M.Company sourceAkamai Noname platform update archiveAkamai states that the Noname Security product is now Akamai API Security.Company sourceAkamai company pageAkamai's current company page reports 2025 annual revenue and worldwide employee scale.Regulatory filing10-K annual filingAkamai Technologies, Inc. (AKAM) · period ended 2025-12-31
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat Akamai application programming interface (API) Security as an application programming interface (API) discovery, posture, testing, analytics, and response candidate with explicit generative AI, large language model (LLM), and Model Context Protocol (MCP) scope; generic application programming interface (API)-security capabilities alone should not be promoted into AI capability coverage.
- Akamai application programming interface (API) Security is platform-agnostic, while inline edge enforcement is documented as a complementary App & application programming interface (API) Protector deployment; validate the entitled enforcement path in the buyer's architecture.
- Akamai Firewall for AI is a separately named product for prompt injection, output filtering, and sensitive-data guardrails. Attribute those controls only when that product is included in scope.
- Use Akamai as the current operating-company context and Noname only as product lineage and an alias.
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
- 4
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 4
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 4
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 4
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 4
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 4
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 4
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 4
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 4
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 4
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 1
- 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.
- 03Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 04Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
- 05Generative AI application security
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
- 06Generative AI application security
A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
- 07AI gateway, tool-connection, and runtime controls
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
- 08AI gateway, tool-connection, and runtime controls
A test policy allows, blocks, transforms, redirects, or rate-limits the request with an explicit reason.
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
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
No supporting claim foundInventory 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
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
Limited 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
No supporting claim foundObserve 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
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.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for workforce discovery of unmanaged AI tools, accounts, users, and usage.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for AI-feature discovery across the enterprise business-application environment.
No quoted source text is recorded for this claim.
Akamai claims discovery and classification of application programming interfaces (APIs) connected to generative AI models, large language models (LLMs), and AI services.
Automatically discover, inventory, and tag all APIs connecting to GenAI models, LLMs, and AI services, including shadow and unmanaged endpoints
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for blocking, coaching, redirecting, or containing unapproved AI use.
No quoted source text is recorded for this claim.
Akamai Firewall for AI claims input and output controls that prevent sensitive-data leakage in AI applications.
Applies multilayered input and output guardrails to prevent sensitive data exposure
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for session-level browser or software as a service (SaaS) controls for employee AI use.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Akamai Firewall for AI claims real-time prompt-injection, jailbreak, adversarial-input, and unsafe-output controls.
Detects and blocks prompt injection, jailbreaks, and adversarial inputs
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for AI inventory, risk, approval, exception, and compliance workflows.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for AI-specific adversarial testing and release assurance.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for model and AI-artifact supply-chain inspection.
No quoted source text is recorded for this claim.
Akamai Firewall for AI offers edge or REST application programming interface (API) integration for AI-application protection.
Integrate via the Akamai edge or REST API for seamless protection across AI applications.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for runtime visibility into agent decisions, actions, tools, and outcomes.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for authorization and control across agent, tool, Model Context Protocol (MCP), or connector handoffs.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for non-human identity and machine-credential lifecycle controls.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for agent registration, delegated authorization, scoped access, and revocation.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for coding-agent, integrated development environment (IDE), CLI, workstation, tool, and package-action governance.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for AI usage attribution, budgeting, anomaly detection, and cost controls.
No quoted source text is recorded for this claim.
Akamai application programming interface (API) Security materials reviewed did not provide a public claim for a public per-user, per-app, usage-based, or enterprise-platform commercial model.
No quoted source text is recorded for this claim.
Akamai positions its application programming interface (API) Security layer as platform-agnostic across distributed application programming interface (API) and AI environments.
API Security is platform-agnostic and works in all environments — SaaS, hybrid, and on-prem