Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI-powered attackers are faster and more systematic than ever. But they still trust what they see. Deception technology controls what they see. 87% of security leaders say AI-related vulnerabilities grew faster than any other risk in 2025 44% year-over-year rise in exploitation of public-facing applications in 2025 300K+ AI platform credentials exposed via infostealer malware on dark web in 2025.

Deception and Claude Mythos is no longer just a cybersecurity conversation. It’s a paradigm change in how organizations need to consider cyber resilience in the era of AI. Enterprises are facing a new threat landscape in which attackers can find exploits quicker, conduct enterprise-wide reconnaissance with low-level automation, and exploit enterprise assets with unprecedented accuracy on an enterprise-wide scale.

Using Cursor, GitHub Copilot, Claude Code, Codex, or another coding agent means giving software access to more than your code. It can also see the credentials available in your workspace, shell, config files, and development environment.

AI desktop assistants and coding tools need credentials to reach external services, and many of them store those credentials as plaintext JSON at predictable paths in the user's home directory. This research covers how credential storage works across 14 popular AI tools, where OS keychain integration is present or missing, and eight attack scenarios that turn that exposure into real risk, from malware-based theft to remote session hijacking to supply-chain compromise via MCP servers.

A platform team finishes a two-week observation window on a new internal research agent. The baseline is stable; the sensor produced a clean profile. By Friday, no policy has shipped — and the blocker isn’t tooling.

The Model Context Protocol (MCP) is a de facto standard for providing structured access to privileged systems for AI agents and external integrations. It acts as a USB-C port for AI, enabling faster innovation by allowing organizations to expose tools, resources, and workflows without the time-consuming work of building APIs. Adoption has surged in recent months, and categories like payments, project management, and developer platforms are already beginning to reap the benefits.

This post is based on Mackenzie's conversation with Noora Ahmed-Moshe on The Secure Disclosure podcast. Listen to the full episode. A company lost a million dollars because someone on a litigation call ran an AI note-taker. As behavioral scientist Noora Ahmed-Moshe explains on the podcast, the tool summarized a confidential conversation and sent it to the opposing party, who used it to force a settlement on their terms.

The Asia-Pacific and Japan (APJ) region, with its dynamic economic growth and technological advancements, presents unique challenges and opportunities in the realm of human risk management and agentic risk management, particularly within the financial services sector. As financial institutions strive to protect themselves from increasing cyber threats, they must align their security practices with the regulations set forth by central banks across the countries.

Most internal AI initiatives fail the same way: someone builds a thing, sends a Slack announcement, runs a lunch-and-learn, and three months later the thing has two active users. The failure mode isn't the AI. It's the ask. Every new surface is a decision engineers have to make: remember to open it, remember to use it, remember to trust it. Seal's approach for our own R&D team was to eliminate the ask entirely. The AI goes where our engineers already are, at the moment they need it.