No solution approach in the current research connects this vendor to this use case.
Vendor research
Imperva AI Application 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
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.
- ~1,400 employees
- Founded 2002
Company context
Privately held Thales company
Imperva says it protects 6,200+ enterprises and employs approximately 1,400 people worldwide
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 6
- Evaluation requirements
- 19 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.
Founded2002
HeadquartersUnited States; city not stated on reviewed current Imperva-controlled pages
OwnershipPrivately held Thales company
Employees~1,400
Capital and scalePlatform provider
Imperva
- Known funding
- Not yet sourced
- Operating scale
- Imperva says it protects 6,200+ enterprises and employs approximately 1,400 people worldwide
- Backing context
- Thales acquired 100% of Imperva from Thoma Bravo for an enterprise value of $3.6B
Thales acquired 100% of Imperva from Thoma Bravo for an enterprise value of $3.6B
Founders and leadershipMore research needed
Founder names and current roles are not yet supported by a public source in this research.
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
- 2002
- Workforce scale
- ~1,400
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- Founder names and current-company status are not yet supported by a public source.
- Private-company funding total is not yet supported by a public source.
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 sourceImperva AI Application Security product pageImperva describes a purpose-built SaaS reverse proxy that evaluates GenAI application inputs and outputs and applies multiple security guardrails.Company sourceThales AI Security Fabric pageThales positions Imperva AI Application Security within its AI Security Fabric and describes prompt, output, and sensitive-data protections for GenAI applications.Company sourceThales acquisition completion releaseThales states that it completed its acquisition of Imperva on December 4, 2023.Company sourceImperva Supplier Code of ConductImperva states that it was established in 2002, is headquartered in the United States, is privately held, and employs approximately 1,400 people worldwide.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Evaluate Imperva AI Application Security as a purpose-built generative AI application runtime control, separately from the broader Imperva Application Security and application programming interface (API) Security portfolio.
- Public product evidence describes inspection of every application-to-large language model (LLM) input and output, with controls for prompt injection, sensitive data, system-prompt leakage, unsafe output, and abusive consumption.
- Confirm deployment, entitlement, supported integrations, logging, and policy-management boundaries during diligence; current public evidence does not support broad agent, Model Context Protocol (MCP), workforce, assurance, or GRC mappings.
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
- 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
- 13
- References
- 31
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 4
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 4
- Related requirements
- 11
- References
- 27
- 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.
- 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
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
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
Limited public supportVisibility, 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.
Imperva AI Application 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.
Imperva AI Application 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.
Imperva claims inspection of inputs and outputs for protected enterprise-built generative AI applications.
analyzing all inputs and outputs
Imperva AI Application 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.
Imperva claims detection, blocking, masking, and alerting for sensitive data in model inputs and outputs.
block, mask, or alert on accidental exposure of PII and sensitive data
Imperva AI Application 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
Imperva claims real-time prompt-injection and jailbreak defense for homegrown generative AI applications.
Dynamically detects and neutralizes user inputs intended to manipulate AI behavior
Imperva AI Application 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.
Imperva AI Application 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.
Imperva AI Application 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.
Imperva claims inline AI guardrails through a software as a service (SaaS) reverse proxy between applications and large language models (LLMs).
operates as a SaaS reverse proxy positioned between your applications and Large Language Models
Imperva AI Application 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.
Imperva AI Application 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.
Imperva AI Application 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.
Imperva AI Application 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.
Imperva AI Application 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.
Imperva claims detection of abusive AI-consumption patterns to prevent runaway costs and denial of service.
prevent “runaway AI costs” and denial-of-service
Imperva AI Application 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.
Imperva positions its AI application control as a model-facing layer deployable in hybrid cloud environments.
seamlessly integrate into any hybrid cloud environment