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EC-Council Expands AI Certification Portfolio to Strengthen U.S. AI Workforce Readiness and Security
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How to Secure Enterprise AI: From Adoption to Incident Readiness
Sygnia-backed guidance urges a lifecycle approach to enterprise AI security, citing survey data that AI adoption is outpacing governance and incident readiness.
The Hacker News published Sygnia-sponsored guidance on securing enterprise AI across its lifecycle, from use-case definition and vendor selection to deployment and incident readiness. It cites Sygnia's 2026 CISO survey of 600 senior leaders: 63% expect AI fully embedded by 2027, 73% say their organization would not be fully ready for a significant cyberattack, and 67% of executives believe unapproved AI tools already caused a breach. The piece highlights shadow AI, ad hoc integrations, and over-permissioned AI agents as key attack surface risks, noting only 38% of organizations report a comprehensive AI policy.
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The inconvenient truth about AI pentesting: someone has to check all the work
Survey of 158 practitioners shows AI pentesting floods teams with findings, creating 'validation debt' most teams cannot process.
The article argues AI pentesting creates 'validation debt': discovery scales far faster than teams' ability to verify AI-generated findings. In a survey of 158 practitioners, only 20.3% had workflows to triage more than 500 AI-generated candidates per engagement, while 29.7% called such volume unmanageable. One respondent spent two days validating 300 AI findings, of which 250 were duplicates, non-exploitable, or nonexistent. The author recommends capacity planning, ruthless deduplication, and risk-based prioritization before adopting AI pentesting tools.
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NIST wants to overhaul its vulnerability database for the AI age
NIST issued a Federal Register RFI seeking public input on overhauling the National Vulnerability Database for AI-scale, machine-consumable security data.
NIST published a request for information arguing the National Vulnerability Database must adapt as LLMs increasingly find and exploit vulnerabilities at machine scale. The RFI seeks input on integrating automation into vulnerability reporting, faster dissemination to defenders, and transparency and auditability in AI-driven decisions. It follows the White House-backed Gold Eagle clearinghouse at Treasury and the VINCE program with Carnegie Mellon's Software Engineering Institute for AI-discovered vulnerability reports.
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Deloitte strengthens AI governance to support trusted enterprise adoption
Deloitte expanded AI Controls and Assurance services to close governance gaps, noting only 21% of firms have mature agentic AI governance.
Deloitte announced expanded AI Controls and Assurance services spanning AI governance frameworks, risk assessments, model validations, AI-enabled internal audit, ecosystem integration with hyperscalers, and regulatory readiness including SOC reporting. The launch cites Deloitte's State of AI in the Enterprise finding that 74% of companies plan to deploy agentic AI within two years while only 21% report mature governance for autonomous agents. The offerings align with Deloitte's Trustworthy AI framework and target the AI lifecycle from exploration to enterprise-scale deployment.
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Powering AI is an architecture problem
Sponsored analysis argues AI data centers need medium-voltage, inline power architecture after Virginia grid faults knocked over 3GW of load offline.
A sponsored MIT Technology Review piece recounts a July 22, 2026 transmission fault in Ashburn, Virginia that shed more than 3 GW of data center load, and a 2024 incident where one failed surge arrester dropped about 60 facilities and 1,500 MW. It argues legacy UPS-based power stacks fail at AI scale because campuses can swing 70% of load in milliseconds and trip offline during grid disturbances. The proposed fix moves protection to medium voltage (13.8 kV and above) in inline enclosures near substations, improving density, permitting timelines, and backup power economics. A full-scale system tested at the DOE National Laboratory of the Rockies cleared ERCOT large-load ride-through requirements.
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ScienceLogic delivers secure AI deployment and smarter IT operations with Skylar AI 2.5
ScienceLogic shipped Skylar AI 2.5 with sovereign, on-premises, and secure-cloud deployment options and expanded AIOps integrations for regulated industries.
ScienceLogic announced Skylar AI 2.5, the intelligence layer of its AIOps platform, adding sovereign cloud, on-premises, and secure cloud deployment options for organizations with strict security, sovereignty, and compliance requirements. The release improves advisory accuracy, analytics and dashboards, AI governance controls such as agent monitoring and token-usage tracking, and integrations with Microsoft Teams and ServiceNow. It targets regulated industries adopting agentic AI, building on the company's FedRAMP Moderate authorization.
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XDOF, just 3 months out of stealth, is in talks for a Series B at a $1.2B valuation
Robotics data startup XDOF is in talks for a Series B at a $1.2B valuation led by 8VC, three months after emerging from stealth.
XDOF, co-founded in 2024 by UC Berkeley researchers Philipp Wu and Fred Shentu, collects real-world teleoperation data for training general-purpose robots. It raised a $70M Series A in June from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital, and annualized revenue is approaching $50 million. The company is partnering with UC Berkeley's AI Research lab to release the ABC robot training dataset and already serves about 20 customers, including several frontier AI labs. Terms of the Series B are not final.
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AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories
At AI Infra Summit, NVIDIA showcased Vera Rubin and DSX gains up to 1.4x tokens per megawatt, plus Annapurna, d-Matrix, and Pinterest partnerships.
Ian Buck's AI Infra Summit keynote before 8,000+ attendees emphasized validated agentic tokens per megawatt as the emerging AI infrastructure metric. Announcements include Amazon's Annapurna Labs collaborating on NVHBM custom high-bandwidth memory, d-Matrix integrating NVLink Fusion with Raptor XPUs, and Pinterest using Blackwell plus Dynamo inference software for conversational visual discovery. Lambda reported 23% better performance per watt with DSX MaxLPS on Blackwell servers, running 19 nodes on a 16-node power budget. NVIDIA says DSX MaxLPS combined with Groq 3 LPX on Vera Rubin NVL72 targets up to 35X token throughput per megawatt versus GB200 NVL72 for 2-trillion-plus-parameter models.
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Anthropic: AI Misuse Is Entering a New Phase: From Cybercrime to Surveillance, Propaganda and Weapons
Anthropic's threat intelligence report documents AI misuse scaling cybercrime, surveillance, propaganda, and weapons development from December 2025 to August 2026.
Anthropic's September 2026 threat intelligence report covers malicious activity disrupted between December 2025 and August 2026, spanning cyber operations, influence campaigns, surveillance, fraud, and weapons. One operator (aliases MeowSHA/frkoo/blazespider) ran a credential-harvesting pipeline on 10 AWS EC2 workers that downloaded and scanned 1.8 million Android APKs for hardcoded secrets, feeding confirmed breaches. Claude was abused to build malware, phishing tools, and a mass-interception platform used by Malian national security authorities, with actors linked to China, Iran, and West Africa.
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Mecka AI nears $500M valuation in Sequoia-led deal amid rush for robot training data
Mecka AI, which collects human motion data for robot training, is nearing a Sequoia-led round at about a $500 million valuation.
Mecka AI is nearing a new funding round led by Sequoia Capital at a valuation of roughly $500 million, three months after raising $60 million led by Framework Ventures with participation from Menlo Ventures, SV Angel, and Kindred Ventures. Founded in 2024 by Josh Gao, Mogen Cheng, Jason Chong, and Duy Nguyen, the startup pays people to record everyday tasks using body sensors and smartphones to produce egocentric training data for humanoid robots. The company projected a $100 million annual run rate by the end of 2026 and competes with firms like Scale AI, Mercor, Surge, Micro1, and XDOF in the physical-world data market.
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The AI Malware Maturity Gap
Recorded Future introduces AIM3, a five-level maturity model for AI malware, showing current attacker AI use is mostly AI-assisted rather than autonomous.
Recorded Future proposes AIM3, a five-level model defining AI malware from LLM-translated to LLM-embedded, spanning experimentation to fully autonomous agentic campaigns. Public examples remain early-stage: PROMPTFLUX uses Google Gemini to rewrite its VBScript dropper (Level 1), while Lamehug/PROMPTSTEAL, attributed to APT28, invokes the HuggingFace API to generate reconnaissance commands (Level 3). The authors argue most current AI malware augments existing tradecraft rather than enabling one-click autonomous attacks.
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Facilitating AI integration with simplicity at scale
Jabil's SAP IT director says simplifying integration across 100+ sites in 30+ countries with SAP Integration Suite created the data backbone for AI.
In an MIT Technology Review Business Lab podcast produced in partnership with SAP, Jabil SAP IT director Harish Manohar described consolidating fragmented tools across more than 100 sites in over 30 countries using SAP Integration Suite. The manufacturer, with 140,000-plus employees and more than 400 top-brand customers, says a standardized data backbone enables real-time supply chain visibility and is a prerequisite for scaling predictive, AI-driven planning and forecasting. The company frames simplification-first modernization as a competitive advantage tied to measurable business value and operational resilience.
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