Audn.AI
AI security platform for offensive security, runtime defense, and compliance across voice, text, and browser interfaces.
Website: https://audn.ai
Cover Block
PUBLIC
| Name | Audn.AI |
| Tagline | AI security platform for offensive security, runtime defense, and compliance across voice, text, and browser interfaces. [Audn.AI homepage, retrieved 2024] |
| Business Model | SaaS |
| Industry | Security |
| Technology | AI / Machine Learning |
| Growth Profile | Venture Scale |
| Founding Team | Other |
Links
PUBLIC
- Website: https://audn.ai/
- LinkedIn: https://www.linkedin.com/posts/oscar-ozkan_advanced-voice-mode-powerful-jailbreak-activity-7357941222776184832-DFjN
Executive Summary
PUBLIC Audn.AI is building a security platform that automates adversarial testing and runtime defense for AI agents, a critical emerging need as enterprises deploy conversational AI in customer-facing roles. The company's immediate wedge is in voice-agent red teaming, where it runs live, multi-turn adversarial calls to identify vulnerabilities like prompt injection and voice cloning before they can be exploited [Audn.AI homepage, retrieved 2024]. Its founding narrative, while not yet independently verified, cites a team of security engineers from Wayve, Meta, and Microsoft, suggesting a technical depth oriented towards the specific challenges of AI systems [Audn.AI homepage, retrieved 2024].
The core product suite, Audn, operates a closed adversarial loop where a Red Team AI attacks an agent and a Blue Team AI validates and scores findings, mapping them directly to compliance frameworks like OWASP and the EU AI Act [Audn.AI homepage, retrieved 2024]. A key differentiator is the inclusion of Pingu Unchained, a 120B-parameter uncensored research LLM specifically trained for red team workflows, which serves as both an internal tool and a standalone offering for security researchers [Audn.AI Pingu Unchained, retrieved 2024]. The business model appears to be SaaS-based, with a publicly listed monthly revenue figure of $1,105 suggesting early, direct commercialization [Trustmrr.com, retrieved 2026].
Over the next 12-18 months, the primary watchpoints will be the validation of its founding team's pedigree through public profiles or announcements, the disclosure of initial enterprise customers or funding partners, and the evolution of its revenue from a nascent figure into a demonstrated growth trajectory. The technical approach is clearly articulated, but external validation of market traction remains the outstanding question.
Taxonomy Snapshot
| Axis | Classification |
|---|---|
| Business Model | SaaS |
| Industry / Vertical | Security |
| Technology Type | AI / Machine Learning |
| Growth Profile | Venture Scale |
| Founding Team | Other |
PUBLIC
Audn.AI presents as a security startup focused on a specific and technically complex wedge: automated adversarial testing for AI agents, with a pronounced emphasis on voice interfaces. The company's public narrative, drawn from its own materials, positions it as a platform built by practitioners to address the security gaps that emerge when AI systems interact with the world through open-ended channels like voice and chat [Audn.AI homepage, retrieved 2024]. Its founding story, as self-reported, cites a team of security engineers from Wayve, Meta, and Microsoft, though no individual founders are named in publicly accessible sources [Audn.AI homepage, retrieved 2024]. The company's early milestones appear to be product-centric, including the launch of its core adversarial validation engine and the development of Pingu Unchained, a 120B-parameter uncensored large language model designed for security research workflows [Audn.AI homepage, retrieved 2024] [Audn.AI blog, retrieved 2024].
Key operational details such as headquarters location, date of incorporation, and legal entity structure are not publicly disclosed. The company's digital presence is anchored by its marketing website and a dedicated research blog, which serves as a platform for publishing technical findings, such as analyses of jailbreaking techniques for AI models [Audn.AI blog, retrieved 2024]. This focus on public research suggests a strategy aimed at establishing technical credibility within the security community, a common early-stage tactic for deep-tech startups before commercial traction becomes the primary signal.
Data Accuracy: YELLOW -- Product claims are confirmed from the company's own website. Founding team background and company origins are self-reported and not independently verified. No external sources confirm incorporation or operational milestones.
Product and Technology
MIXED
Audn.AI positions its core product as a continuous adversarial validation engine for AI agents, a system that operates more like an automated security operations center than a point-in-time scanner. The platform runs what the company calls a closed adversarial loop, where a Red Team AI attacks an agent while a Blue Team AI validates and scores each finding, with results logged and fed back into the system [Audn.AI homepage, retrieved 2024]. This process is designed to run thousands of adversarial turns per day across text and voice interfaces, aiming to provide persistent runtime defense rather than periodic audit compliance [Audn.AI homepage, retrieved 2024].
The product architecture is presented through four integrated tools, each serving as an entry point to the same validation engine [Audn.AI homepage, retrieved 2024].
- Audn Red. Described as an attack corpus containing over 50,000 techniques, with over 500 new techniques added daily. The company claims it has tested over 200 models with a 94% success rate in vulnerability detection [Audn.AI Audn Red, retrieved 2024].
- Audn Purple. Framed as an RL-SEC hardening loop, presumably using reinforcement learning to iteratively improve agent security based on attack findings.
- Audn Blue. Positioned as a real-time defense system.
- Audn Red Voice. A specialized module for voice-agent red teaming, which the company highlights as a clear product wedge. This tool runs live, multi-turn adversarial calls against AI agents, testing for vulnerabilities like deepfake voice injection, social engineering, IVR escape, and OTP theft [Audn.AI - $1105 last 30 days, retrieved 2026]. It integrates with communication platforms like Twilio, Genesys, and Amazon Connect [Audn.AI Voice AI Red Teaming, retrieved 2024].
A significant and publicly accessible component of the technology stack is Pingu Unchained, a 120B-parameter uncensored research LLM [Audn.AI homepage, retrieved 2024]. The company states this model is specifically trained for red team and pentest workflows, understanding security context to ask questions mainstream LLMs are trained not to answer [InfoWorld, retrieved 2026] [Reddit, retrieved 2026]. It is offered alongside isolated virtual machines with fresh IPs for secure testing [Audn.AI Pingu Pricing, retrieved 2024]. The platform's output is structured for compliance, mapping all findings to frameworks like OWASP, NIST, and the EU AI Act, and delivering an audit-ready evidence pack within 24 hours [Audn.AI homepage, retrieved 2024] [Audn.AI - $1105 last 30 days, retrieved 2026].
Data Accuracy: YELLOW -- Product claims are detailed and self-consistent across the company's primary sources, but lack independent technical validation or published case studies.
Market Research
PUBLIC
The urgency for specialized AI security tooling is no longer speculative, but a direct consequence of enterprise deployments moving from controlled pilot to open-ended, customer-facing production. The market Audn.AI targets is defined by the convergence of three distinct but overlapping pressures: the rapid adoption of generative AI agents, the escalating regulatory scrutiny of AI systems, and the novel attack surfaces these systems introduce, particularly in voice and multimodal interfaces.
Third-party market sizing specifically for AI agent security remains nascent, but analogous public reports illustrate the scale of the underlying demand. The global AI security market was valued at $2.5 billion in 2023 and is projected to reach $12 billion by 2028, growing at a compound annual rate of 36% [MarketsandMarkets, 2024]. More granularly, the market for AI governance, risk, and compliance (GRC) software, which includes compliance mapping and evidence generation, is forecast to grow from $1.1 billion in 2023 to $4.7 billion by 2028 [Gartner, 2024]. These figures provide a proxy for the broader ecosystem Audn.AI operates within, though its specific wedge into automated adversarial testing and voice agent red-teaming represents a narrower, fast-emerging segment.
Demand drivers are both technical and regulatory. On the technical side, the shift from static API calls to persistent, multi-turn AI agents creates complex, stateful decision flows that traditional application security tools cannot instrument or protect. The cited research highlights novel attack vectors like prompt injection, voice cloning, and social engineering at scale, which require continuous, adversarial simulation to detect [Audn.AI homepage, 2024]. Regulatory tailwinds are equally significant. Frameworks like the EU AI Act, NIST AI Risk Management Framework, and sector-specific guidelines (e.g., PCI DSS for payments) are mandating documented risk assessments and evidence of security controls for high-risk AI systems. This creates a direct compliance budget for tools that can automate testing and generate audit-ready reports, a capability Audn.AI explicitly promotes [Audn.AI - $1105 last 30 days, 2026].
Key adjacent markets include traditional application security testing (AST), which Audn.AI positions as insufficient for agentic systems, and the broader AI development platform and MLOps sector. Substitutes could involve manual red-team services or in-house security research teams building custom tooling, though these lack the scalability and continuous validation Audn.AI offers. The primary macro risk is a potential slowdown in enterprise AI agent adoption, which would dampen immediate demand. Conversely, a high-profile security breach involving a voice AI agent could accelerate market awareness and spending.
AI Security Market 2023 | 2.5 | $B
AI Security Market 2028 | 12 | $B
AI GRC Software 2023 | 1.1 | $B
AI GRC Software 2028 | 4.7 | $B
The projected growth rates, while for analogous markets, underscore the substantial budget allocation flowing toward securing and governing AI systems. For Audn.AI, the critical question is whether its focused technical approach on offensive security and compliance automation can capture a meaningful portion of this spend before larger platform vendors expand their own offerings into the space.
Data Accuracy: YELLOW -- Market sizing is drawn from analogous, third-party analyst reports; specific segmentation for AI agent red-teaming is not publicly available.
Competitive Landscape
MIXED Audn.AI enters a competitive landscape defined by established platform security vendors expanding into AI and a new wave of startups targeting AI-specific vulnerabilities, carving out a niche with its focus on automated, multi-turn adversarial testing, particularly for voice agents.
The competitive map can be segmented into three primary categories. First, broad-spectrum application security platforms like Snyk and Wiz have introduced AI security modules, but these typically focus on scanning code repositories and infrastructure for known vulnerabilities in AI/ML pipelines rather than simulating novel, interactive attacks against live agents [Snyk, 2024] [Wiz, 2024]. Second, dedicated AI security startups such as Lakera (focused on prompt injection defense) and Protect AI (securing the ML supply chain) address specific threat vectors but do not offer the integrated red team/blue team simulation loop that Audn.AI describes as its core engine [Lakera, 2024] [Protect AI, 2024]. Third, adjacent substitutes include traditional penetration testing services and consultancies, which offer manual red teaming but lack the continuous, automated scale Audn.AI claims to provide.
Audn.AI's current defensible edge appears to be its proprietary attack corpus and its integrated, adversarial loop architecture. The company claims its Audn Red tool contains over 50,000 attack techniques, with over 500 new ones added daily, and has tested over 200 models with a 94% success rate in vulnerability detection [Audn.AI Audn Red, retrieved 2024]. This dataset, built for and by its closed-loop system, could create a short-term technical moat. Furthermore, its provision of Pingu Unchained, a 120B-parameter uncensored research LLM, positions it as a tool provider for security researchers, potentially creating a developer-led adoption funnel [Audn.AI Pingu Unchained, retrieved 2024]. However, this edge is perishable; attack techniques can be reverse-engineered or independently discovered, and larger platforms could replicate the adversarial testing concept with greater resources and existing customer relationships.
The company is most exposed in two areas. It lacks the enterprise sales motion and compliance certification depth of incumbents like Palo Alto Networks or CrowdStrike, which are rapidly integrating AI security into their broader platforms. A competitor like Wiz, with its established cloud-native application protection platform (CNAPP) and large enterprise footprint, could quickly launch a similar adversarial testing module and outflank Audn.AI on distribution [Wiz, 2024]. Secondly, the focus on voice agent security, while a clear wedge, is a relatively nascent sub-segment. If enterprise adoption of voice AI agents slows or consolidates around a few large platform providers (e.g., Amazon, Google) that bake in their own security, Audn.AI's initial market could remain niche.
The most plausible 18-month scenario hinges on the pace of AI agent deployment and regulatory pressure. If the EU AI Act and similar regulations enforce stringent adversarial testing requirements for high-risk AI systems, a vendor like Audn.AI that maps findings directly to these frameworks could become a compliance necessity [Audn.AI homepage, retrieved 2024]. In this scenario, a winner would be a company that successfully transitions from a technical tool to a mandated compliance layer. Conversely, if the market consolidates around end-to-end platforms, the loser would be a point solution like Audn.AI that fails to expand beyond its initial wedge or secure a strategic partnership with a major cloud provider or security incumbent.
Data Accuracy: YELLOW -- Competitive analysis is inferred from public descriptions of adjacent players; no direct competitor citations are available for Audn.AI.
Opportunity
PUBLIC
The prize for Audn.AI is the role of default security infrastructure for the emerging class of production AI agents, a position that could command enterprise-scale contracts as deployments move from pilot to mission-critical.
The headline opportunity is to become the category-defining security platform for AI agents, analogous to what CrowdStrike or Palo Alto Networks became for endpoint and network security. The company’s early wedge into automated voice-agent red teaming provides a concrete entry point into a broader problem: securing open-ended, conversational AI interfaces that traditional code-scanning tools cannot see. The platform’s stated ability to map vulnerabilities to major compliance frameworks like OWASP, NIST, and the EU AI Act [Audn.AI homepage, retrieved 2024] directly addresses a pressing need for auditability as regulations tighten. This combination of automated offensive testing and compliance reporting creates a path to becoming the mandated security layer for any enterprise deploying customer-facing AI agents.
Growth could follow several distinct, plausible paths, each anchored in the company’s current technical focus.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Voice Security Standard | Audn.AI becomes the mandated red-teaming provider for all voice AI deployments in regulated industries (finance, healthcare). | A major breach of a voice AI system leads to regulatory guidance explicitly recommending continuous adversarial testing. | The platform already integrates with major voice infrastructure like Twilio, Genesys, and Amazon Connect [Audn.AI Voice AI Red Teaming, retrieved 2024], demonstrating a ready technical path to becoming an embedded security check. |
| Compliance Engine | The company’s automated evidence packs become the de facto method for proving AI agent compliance under the EU AI Act and similar regimes. | A wave of enforcement actions creates urgent demand for turnkey compliance tooling from legal and security teams. | Audn.AI already advertises delivering an audit-ready evidence pack mapped to controls in 24 hours [Audn.AI - $1105 last 30 days, retrieved 2026], positioning it as a compliance utility rather than just a testing tool. |
| Research Platform | Pingu Unchained, the company’s 120B-parameter uncensored LLM, becomes the go-to tool for security researchers, creating a developer-led adoption funnel. | A high-profile security research paper credits Pingu Unchained with a novel AI vulnerability discovery. | The model is already described as specifically trained for red team/pentest workflows [Which AI should i use to learn? |
What compounding looks like is a data-driven flywheel. Each new customer deployment feeds the core adversarial engine, Audn Red, with new attack surfaces and failure modes. The company claims this corpus already contains over 50,000 techniques, with over 500 new ones added daily [Audn.AI Audn Red, retrieved 2024]. As this proprietary dataset of live attack vectors grows, the platform’s ability to find novel vulnerabilities before attackers do improves, creating a technical moat. This, in turn, makes the compliance mappings more authoritative and the real-time guardrails (Audn Blue) more effective, increasing switching costs for existing customers and raising barriers for new entrants.
The size of the win can be framed by looking at established security platforms. CrowdStrike, a leader in endpoint security, currently holds a market capitalization over $90 billion. A more direct, though earlier-stage, comparable is Wiz, a cloud security platform that reached a $10 billion valuation within a few years of founding. If Audn.AI successfully executes on the "Compliance Engine" scenario and captures a material portion of the AI security and governance market,a segment that Gartner has flagged as a top priority for CIOs,a multi-billion dollar outcome is within the realm of possibility. This is a scenario-based outcome, not a forecast, but it illustrates the scale of the opportunity if the company can transition from a technical tool to an enterprise platform.
Data Accuracy: YELLOW -- The opportunity analysis is based on the company's published product capabilities and integration claims, which are consistently cited from its own materials. The growth scenarios are plausible extrapolations from these capabilities but lack external validation from customer case studies or market analyst reports.
Sources
PUBLIC
[Audn.AI homepage, retrieved 2024] Audn.AI | https://audn.ai/
[Audn.AI Audn Red, retrieved 2024] Audn Red - AI Penetration Testing & Attack Corpus | https://audn.ai/audn-red
[Audn.AI Pingu Unchained, retrieved 2024] Introducing Pingu Unchained: The Unrestricted LLM for High-Risk Research | Audn.AI Security Research | https://blog.audn.ai/posts/pingu-unchained
[Audn.AI blog, retrieved 2024] AI Security Research & Red Teaming | Audn.AI Security Research | https://blog.audn.ai/
[Audn.AI Voice AI Red Teaming, retrieved 2024] Voice AI Red Teaming | https://audn.ai/red-voice
[Audn.AI Pingu Pricing, retrieved 2024] Pingu Pricing | https://pingu.audn.ai/billing
[Audn.AI - $1105 last 30 days, retrieved 2026] Audn.AI - $1105 last 30 days | https://trustmrr.com/startup/audn-ai
[InfoWorld, retrieved 2026] 19 large language models redefining AI safety,and danger | InfoWorld | https://www.infoworld.com/article/3719999/19-large-language-models-redefining-ai-safety-and-danger.html
[Which AI should i use to learn? | r/cybersecurity on Reddit, retrieved 2026] Which AI should i use to learn? | r/cybersecurity on Reddit | https://www.reddit.com/r/cybersecurity/comments/1nukeiw/yesterday_i_was_using_ai_to_persuade_another_ai/
[MarketsandMarkets, 2024] AI Security Market Size, Share, Industry Trends, Forecast 2028 | MarketsandMarkets | https://www.marketsandmarkets.com/Market-Reports/ai-security-market-220634996.html
[Gartner, 2024] Gartner Forecasts Worldwide AI Governance, Risk and Compliance Software Market to Reach $4.7 Billion by 2028 | Gartner | https://www.gartner.com/en/newsroom/press-releases/2024-10-08-gartner-forecasts-worldwide-ai-governance-risk-and-compliance-software-market-to-reach-4-7-billion-by-2028
[Snyk, 2024] Snyk AI Security | https://snyk.io/product/ai-security/
[Wiz, 2024] Wiz AI Security Posture Management | https://www.wiz.io/solutions/ai-security
[Lakera, 2024] Lakera Guard | https://www.lakera.ai/lakera-guard
[Protect AI, 2024] Protect AI | https://protectai.com/
[Audn.AI Security Research, retrieved 2024] Jailbreaking Sora 2: When AI Safety Becomes a Remix Problem | Audn.AI Security Research | https://blog.audn.ai/posts/jailbreaking-sora-2-remix-problem
Articles about Audn.AI
- Audn.AI's 120B-Parameter Red Team Aims to Jailbreak the Voice Agent — The security startup is running thousands of adversarial calls a day to find the vulnerabilities in your AI's tone of voice.