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Google has introduced “Skills” in Chrome, enabling users to save and reuse Gemini prompts as repeatable workflows across websites. The feature transforms one-off AI interactions into reusable automation layers, allowing users to execute complex tasks with a single click. Positioned as a productivity upgrade, Skills aim to streamline repetitive workflows and improve efficiency across day-to-day operations.

Source: TechCrunch

What to know:

• Gemini Skills allow users to save prompts and reuse them across tabs and websites, effectively turning AI interactions into repeatable workflows.
• The feature integrates directly into Chrome, making AI contextually available across browsing sessions, rather than confined to a single interface.
• Users can create custom workflows for tasks like summarization, data extraction, content generation, and repetitive operational processes.
• Skills reduce the need for repeated prompting, improving speed, consistency, and standardization of outputs across teams.
• The shift from ad-hoc prompting to reusable workflows signals a move toward embedded AI automation within everyday business tools.

Why it matters:

The introduction of reusable AI workflows through Gemini Skills shifts enterprise risk from individual prompt usage to persistent, scalable automation embedded within daily operations. As prompts evolve into reusable assets, organizations lose visibility into how AI is being applied across teams, increasing the risk of sensitive data exposure, inconsistent outputs, and unintended workflow propagation. This creates a new governance challenge where monitoring AI behavior, enforcing usage boundaries, and maintaining control over prompt-driven processes becomes essential. For businesses adopting GenAI at scale, the focus must move beyond access control to include continuous observability, prompt-level risk assessment, and proactive monitoring of AI-driven workflows to prevent silent, system-wide vulnerabilities.

Prompt injection has become OWASP's top-ranked risk category for GenAI applications as adoption in state and territorial government environments reached 82% of employees using AI in daily work, up from 53% the prior year, according to a 2025 NASCIO survey of 51 CIOs. A Center for Internet Security (CIS) report identifies a fundamental architectural weakness: language models cannot separate instructions from other data, processing embedded malicious instructions in external content in the same way as normal requests.

Source: Help Net Security

What to know:

• LLMs process input without distinguishing between instructions and data, enabling direct prompt injection through model interaction and indirect injection via malicious instructions embedded in web pages, emails, or documents that AI systems later retrieve and process.
• GenAI tools with privileged access to systems and data can be manipulated to poison agentic databases across user sessions, contaminate external datastores like cloud storage and email inboxes, and potentially execute code on behalf of attackers.
• An Amazon Q extension update for Visual Studio Code in July 2025 inadvertently introduced a prompt that could instruct the AI agent to delete files and terminate servers; AWS patched within two days and issued a security bulletin.
• The Morris II worm demonstrated propagation patterns by embedding malicious prompts in emails that entered RAG databases through AI email assistants, which then generated additional emails containing similar payloads along with sensitive information.
• Research traces prompt injection vulnerabilities back to 2013, with studies indicating that targeted training improves model handling but does not provide sufficient protection against attacks that exploit fundamental limitations in how language models process input.

Why it matters:

Prompt injection represents a detection and monitoring challenge for organizations embedding GenAI across operational workflows. AI systems with privileged access to enterprise data create exposure through attack vectors that differ from conventional threats, requiring visibility into AI behavior patterns, inventory management of AI system permissions, least privilege enforcement, and continuous monitoring to identify anomalous activity. Understanding which systems AI can reach and what data it processes becomes essential for risk assessment and early detection of potential security incidents.