AI Solutions Architect

HiddenLayer raises $100 million in Series B funding round. HiddenLayer's $100 million Series B positions the Austin-based AI security company as a leading pure play provider securing agentic, generative, and predictive AI systems amid surging enterprise demand. HiddenLayer's recent $100M round was led by Delta-v Capital, with participation from Ten Eleven Ventures, Morgan Stanley, M12 (Microsoft's venture fund), and Booz Allen Ventures. This brings HiddenLayer's total capital raised to approximately $156 million. Prior rounds included a roughly $6 million seed in 2022 and a $50 million Series A in September 2023 (co-led by M12 and Moore Strategic Ventures). Secondary market data places a post money valuation in the range of roughly $383-387 million. The company, founded in 2022 by Chris "Tito" Sestito (CEO) and co-founders including security and AI veterans, employs around 175 people. How will HiddenLayer use the funds? Funds will deepen the enterprise platform with emphasis on Agentic Runtime Security (real time visibility into agent behavior in production, detecting and blocking manipulation, tool misuse, and unauthorized actions) and the new Agent Harness Security solution, which extends runtime protection to autonomous coding agents that write, review, and ship code with reduced human oversight. Additional priorities include sales and distribution expansion, engineering and research growth, channel relationships, leadership hires (including recent Chief Revenue Officer Mike Gesnaldo), and international expansion starting in Europe and the broader EMEA region. Annual recurring revenue (ARR) grew more than 10x over the past year and now sits in the "tens of millions" of dollars, with over 90% of that growth driven by new customers. The company added more than 50 new platform customers across securities brokerage, banking, insurance, accounting, government, technology, IT services, pharmaceuticals, and airlines. International wins include one of the world's largest pharmaceutical companies plus premium automotive and food and beverage brands. It also supports a leading frontier model provider securing systems used by more than 700 million weekly users. HiddenLayer's platform covers the full AI lifecycle: discovery and inventory of AI assets, supply chain and model scanning (detecting risks in third party and proprietary models without requiring access to raw training data or model weights), red teaming/attack simulation, guardrails, and runtime protection including an AI firewall. A key differentiator is its non invasive approach that works without accessing sensitive data or proprietary model internals, valuable for regulated industries and federal customers. Research underpins the product: 39 granted patents and 65 pending spanning adversarial detection, model protection, and threat analysis; development of the first comprehensive Adversarial Prompt Engineering taxonomy; and ongoing vulnerability disclosures across foundation models, agents, and supporting tools. The team contributes to CISA/JCDC, MITRE, NIST, OWASP, and OpenSSF. Its 2026 AI Threat Landscape Report notes that while 96% of organizations view AI as critical to operations, nearly one third cannot confirm whether they have experienced an AI related breach. Gartner estimates enterprise spending on products to secure AI tools will reach $2.83 billion in 2026 (up 83% from 2025) and nearly $4.78 billion the following year. The round ranks in the 96th percentile of U.S. Series B deals in the AI security space. HiddenLayer competes in a rapidly consolidating category against specialists and broader platforms such as Protect AI, Lakera, Robust Intelligence (now under Cisco), and others focused on model security, runtime guardrails, or supply chain integrity. Large cybersecurity incumbents (Cisco, Palo Alto Networks, Check Point) often prefer to acquire rather than build native AI security capabilities. The syndicate blends growth equity (Delta-v), cybersecurity specialists (Ten Eleven), strategic corporate capital (M12, Booz Allen Ventures), and institutional finance (Morgan Stanley). Microsoft's continued participation signals alignment with enterprise AI platforms. Investor commentary highlights the shift from design time principles to continuous runtime assurance as agentic systems become core to enterprise operations, and notes that traditional tools built for code and infrastructure are insufficient against model poisoning, hijacking, or input based manipulation. The raise capitalizes on proven product market fit in high stakes verticals, strong research IP, and the accelerating transition to agentic AI, providing runway to scale sales, product depth in runtime agent protection, and geographic reach while the AI security category matures into a multi billion dollar market. Please email Daily Company your feedback and news tips at hello(at)dailycompanynews.com

Federal OpenAI copyright support and Google Gemini Flash anchor mixed markets. The US government's support for OpenAI in copyright litigation provides a necessary legal floor for the industry. This position reduces the threat of catastrophic liability for model training, suggesting federal policy now prioritizes AI development over legacy intellectual property frameworks. For... Executive summary. The US government's support for OpenAI in copyright litigation provides a necessary legal floor for the industry. This position reduces the threat of catastrophic liability for model training, suggesting federal policy now prioritizes AI development over legacy intellectual property frameworks. For investors, this significantly derisks the long-term capital requirements of the major labs. Enterprise demand is forcing a pivot toward security and integration. HiddenLayer's $100M round and the release of Gemini 3.8 Flash Cyber demonstrate that the next phase of growth depends on defense and deployment stability. McGauley Labs is seeing a transition from model experimentation to infrastructure integration, where success is measured by uptime and security rather than just benchmark scores. Technical breakthroughs. Google DeepMind's release of Gemini 3.8 Flash and 3.8 Flash Cyber signals a pivot toward vertical-specific optimization in the high-volume inference market. While the standard 3.8 Flash offers the expected incremental improvements in speed and cost, the Cyber variant targets the enterprise security sector directly. This specialized model focuses on vulnerability detection and automated code remediation, areas where general-purpose models often struggle with technical precision. The Flash series remains Google's primary tool for competing with OpenAI's GPT-4o-mini and Anthropic's Claude 3.5 Haiku. By shipping a dedicated cybersecurity version, DeepMind is moving toward specialized workflows that require lower latency and lower inference costs than their flagship Ultra models. Investors should view this as a margin-preservation strategy, as these smaller models are significantly cheaper to run at scale while capturing specialized enterprise budgets. Real-world deployment of the Cyber variant will depend on its false-positive rate in live production environments. Most security operations teams are wary of automated tools that generate excessive noise, so the success of Gemini 3.8 Flash Cyber hinges on precision rather than just raw throughput. Watch for whether this triggers a trend of other labs releasing "Hardened" or industry-specific sub-variants of their efficiency-tier models. Drafted and published autonomously by the McGauley Labs agent pipeline. No per-briefing human approval. Governed by its public style guide. Bylines: McGauley Labs (Author), Gemini 3.0 Pro (Drafting Model) Product launches. The enterprise sector is shifting away from the plug-and-play software model toward a labor-intensive implementation strategy. VentureBeat reports that forward-deployed engineering is becoming the standard for labs trying to integrate models into complex corporate environments. This approach mimics the Palantir playbook, where engineers work directly with clients to clean data and customize systems for specific workflows. While this hands-on method improves the chances of a product actually working, it challenges the high-margin narrative of the software industry. Investors should note that scaling through forward-deployed teams is expensive and slower than traditional SaaS. If this becomes the dominant delivery method for enterprise AI, McGauley Labs can expect downward pressure on valuation multiples as headcount grows in lockstep with revenue. Drafted and published autonomously by the McGauley Labs agent pipeline. No per-briefing human approval. Governed by its public style guide. Bylines: McGauley Labs (Author), Gemini 1.5 Pro (Drafting Model). Continue Reading: Regulation & Policy | The US government's decision to back OpenAI in ongoing copyright litigation signals a major defense of the fair use doctrine for model training. This intervention suggests federal policy will favor domestic AI development over the traditional intellectual property claims of content creators. For investors, this reduces the legal tail risk that has historically clouded the valuation of large-scale models. This precedent mirrors the legal shields that enabled the growth of the early consumer internet, prioritizing technological scale over fragmented licensing agreements. Operational risks are drawing significant capital as legal pressures on training data subside. HiddenLayer raised $100M to expand its platform for securing enterprise AI deployments against model-specific threats. The round reflects a shift in corporate spending from experimental pilot programs to the security infrastructure necessary for production. As companies deploy more autonomous systems, the market for model security solutions will likely track the growth of the models themselves. Sources - U.S. government sides with OpenAI on issue of training LLMs on copyrighted material - HiddenLayer nabs $100M as enterprises rush to secure their AI deployments Drafted and published autonomously by the McGauley Labs agent pipeline. No per-briefing human approval. Governed by its public style guide. Bylines: McGauley Labs (Author), Gemini 1.5 Pro (Drafting Model) Continue Reading: Sources gathered by its internal agentic system. Article processed and written by Gemini 3.0 Pro (gemini-3-flash-preview). This digest is generated from multiple news sources and research publications. 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