Home / AI / Shipping 10–50× More Code? Watch This Webinar on Securing AI-Speed…
Shipping 10–50× More Code? Watch This Webinar on Securing AI-Speed Development
thehackernews.com Aug 10, 2026

Shipping 10–50× More Code? Watch This Webinar on Securing AI-Speed Development

Curated by · TechCapsules ·

AI-summarised brief · reviewed before publication

The webinar “The True Cost of Building at Machine Speed” addresses the challenge of maintaining security as AI-driven development multiplies code output by 10–50 times. Chainguard experts explain that traditional vulnerability‑management cycles can become bottlenecks when code volume surges, and that attackers can also leverage AI to craft more sophisticated exploits. The session outlines how to shift from CVE‑centric remediation to secure‑by‑default practices, establish new guardrails, and align risk ownership with executive expectations. It offers a framework for scaling security controls to match AI‑accelerated development.

💡 Why It Matters

  • · Rapid code generation forces security teams to rethink risk governance, or risk will outpace remediation and expose critical assets.
AI Capsules Cybersecurity
Read full article on thehackernews.com
More in AI →
Kimsuky Builds Offline AI Stack to Boost Phishing and Automate Malware Development
thehackernews.com · Aug 10

Kimsuky Builds Offline AI Stack to Boost Phishing and Automate Malware Development

North Korean espionage unit Kimsuky has begun running artificial‑intelligence models on isolated servers, according to South Korean security firm Genians. Researchers uncovered offline stacks that include Ollama, GPT‑4All, and Msty, as well as retrieval‑augmented generation databases, developer libraries such as LLaMaSharp and Microsoft’s Semantic Kernel, and OpenAI Whisper tools. The tools were configured to link private document collections to language models, suggesting the group is testing AI‑assisted phishing and malware creation rather than training its own model. Genians linked the activity to the Operation GitPower campaign, which uses GitHub repositories to deliver LNK‑to‑PowerShell infection chains and encrypted AsyncRAT payloads. No evidence yet shows the stack deployed against victims, but the setup indicates a shift toward faster, less detectable attacks overall.

💡 By moving AI off public services, Kimsuky can generate phishing lures and malware without leaving the typical chatbot footprints, forcing defenders to abandon content‑based cues and monitor low‑level execution artifacts instead.

The Devil Is Not in the Machine
marketingmagazineasia.com · Aug 10

The Devil Is Not in the Machine

A new editorial argues that artificial intelligence is being cast as the villain of the creative sector, yet the industry’s push for mass‑produced, low‑cost content predates AI. The piece contends that procurement, platform pressures, quarterly targets and shrinking retainers have already driven the industrialisation of creativity, turning designers and writers into “human resizing machines.” While AI can now generate headlines, layouts, synthetic voices and product images at speed, agencies such as WPP Open and Publicis are integrating AI agents into strategy, creative, media and production, treating the technology as a core operational tool. The article warns that demonising AI distracts from deeper questions about leadership decisions that prioritize efficiency over quality, fair workloads and genuine creative development, and it highlights ongoing legal concerns over copyright and compensation for AI‑trained works.

💡 The debate reveals that the real threat to creative work lies in profit‑driven management choices, not the technology itself.

How AI Is Reshaping Semiconductor Design and the Emerging Questions Around Human Inventorship
semiconductor-digest.com · Aug 10

How AI Is Reshaping Semiconductor Design and the Emerging Questions Around Human…

AI’s role in semiconductor design has shifted from simple assistance to “agentic” systems that can interpret specifications, generate RTL code, create verification environments and iterate fixes with minimal human input. The transition was highlighted by two March 2026 announcements: Synopsys’s AgentEngineer, which claims 2‑5× faster front‑end design cycles that normally span four to six months, and a collaborative effort led by NVIDIA that equips Cadence, Dassault Systèmes, Siemens and Synopsys with similar tooling. This evolution raises a legal dilemma because U.S. patent law permits only human inventors, and USPTO guidance—reinforced by the Thaler v. Vidal decision—excludes AI from inventorship. The key question is whether a human designer can credibly claim conception when they merely select or reject AI‑generated options. Kudla advises design houses to treat inventorship documentation as an engineering discipline, recording which AI outputs were dismissed, why, and how engineers redirected the tools, to satisfy patent requirements amid divergent international standards and growing pressure to adopt agentic EDA solutions.

💡 Proper documentation will determine whether breakthrough chip designs can be patented, directly affecting companies’ ability to protect and monetize AI‑driven innovations.

How to build an AI agent: A simple guide for anyone
news.microsoft.com · Aug 10

How to build an AI agent: A simple guide for anyone

An AI agent can automatically detect and respond to security incidents, such as a phishing‑induced compromised account, by disabling the account, scanning for malware, opening tickets, alerting staff and drafting reports. Unlike chat‑based AI, agents act on tasks and integrate with Microsoft 365 Copilot, allowing non‑developers to create functional prototypes without code. The guide outlines four steps: (1) define a concrete business problem and desired outcome; (2) explore existing models or pre‑built agents in the Copilot store and assess skill level; (3) use the “New Agent” feature to describe the task in plain language, generate a draft, test, and refine behavior, tone and workflow; and (4) attach relevant data sources and specify output formats such as reports or email replies. The process enables teams to automate repetitive work like status‑report generation or shared‑inbox handling.

💡 By turning everyday workflows into autonomous actions, AI agents let organizations cut manual effort and tighten security response times without requiring software‑development expertise.

Google’s AI faces pushback over use of web content for AI answers
business-standard.com · Aug 10

Google’s AI faces pushback over use of web content for AI answers

Google’s web crawler, Googlebot, is now feeding both its search index and its Gemini AI model, allowing the company to scrape roughly three times more of the internet than OpenAI’s crawler and nearly five times more than Microsoft’s, according to Cloudflare CEO Matthew Prince. The practice blurs the line between content collected for search and material used to train AI, giving Google a competitive edge while making it harder for publishers to block AI training without sacrificing search traffic. British regulators and an industry challenger have begun pushing back, arguing the approach creates an unfair advantage and fuels a surge in automated web traffic that now exceeds human activity, as reported by Cloudflare data for 2026.

💡 The unchecked merger of search and AI training threatens publishers’ control over their content and reshapes the balance of power in the AI market.

The On-Chain AI Skill Economy · The Standard Layer
nynewscast.com · Aug 10

The On-Chain AI Skill Economy · The Standard Layer

FinChip announced that its ERC‑8338 proposal, titled “Token‑Bound Executable Skills,” has been assigned an official Ethereum ERC number and filed in the ethereum/ERCs repository as PR #1879. Authored by CEO Gary Yang, the draft standard defines the foundational layer for an on‑chain AI skill economy, enabling verifiable ownership and execution of AI “skills” on the blockchain. A CC0 reference implementation, a live testnet contract, and six reproducible test vectors are now publicly available. FinChip argues that current AI skill distribution lacks integrity, as files can be altered without detection, posing security risks. By providing a transparent, checkable standard, FinChip aims to establish a trusted infrastructure before the market’s rapid growth, positioning itself as a pioneer in the emerging skill‑centric ecosystem.

💡 It gives developers a tamper‑proof way to certify the exact AI instructions their agents run, closing a critical security gap as the on‑chain skill market expands.