Transparency scores are falling, hallucination rates on user-framed statements hit as high as 94%, and benchmark performance still fails to predict real-world results. The gap between what AI can do and what organizations can actually verify is now the problem worth solving...
As organizations rush to move AI into production, they’re finding that the tools they rely on to monitor traditional software don’t translate cleanly to AI systems. The reason is fundamental: AI doesn’t fail as software does. It doesn’t throw clean error codes or follow predictable execution paths. It drifts, hallucinates, and degrades in ways that are often subtle, intermittent, and hard to reproduce.
The result is a growing gap between what teams think observability should provide and what current tools actually deliver. The uncomfortable truth? The AI observability tools we have today are built for yesterday’s problems.
To understand where the industry is headed, we need to look at where it is today and why that’s not enough.
AI observability today: The era of evals
Today’s AI observability landscape is dominated by one concept: evaluation.
Most tools focus on scoring model outputs after the fact. They rely on test datasets, human graders, or, increasingly, “LLM-as-a-judge” approach
Insider Brief Verkada announced Nvidia has made an investment in the company and is working with it on AI systems to bring its physical security platform to more devices. Verkada said the goal is to bring more context-aware AI into the built environment, where cameras and sensors can help organizations manage safety, security and operations […]
AI coding tools' vulnerability to indirect prompt injection necessitates enhanced security measures, posing new risks for developers and organizations.
The post New attack exploits Claude Code to hijack developer machines through poisoned repositories appeared first on Crypto Briefing.
Enterprise investment in AI is booming. Gartner is calling 2026 an “inflection year” for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. A prime opportunity for AI agents…
The rapid evolution of AI-enabled cyber threats necessitates urgent advancements in AI-specific defenses to protect digital assets effectively.
The post CrowdStrike reports 90 organizations targeted by prompt injection attacks in 2025 appeared first on Crypto Briefing.
Our 2026 State of AI for Business Report surveyed more than 2,100 business professionals, including nearly a third who are marketers and 84% who work for B2B organizations.
Respondents said 41% of their organizations describe their AI momentum as inconsistent or siloed. More than half of individual professionals have moved past experimentation, yet their organizations haven't caught up.
OpenAI introduces new Daybreak tools, including Codex Security and GPT-5.5-Cyber, to help organizations find, validate, and patch vulnerabilities at scale.
