Can iPhones Get Viruses? (The Truth About iOS Security)
Think your iPhone is invincible against viruses? Not so fast! While Apple devices have strong built-in security, they are far from invulnerable to modern cyber threats.
Security | Threat Detection | Cyberattacks | DevSecOps | Compliance
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Auditing Agentic Behavior for FedRAMP Compliance | Teleport
AI agents are tireless, highly capable, eager to please, but difficult to manage. George Chamales (CriticalSec) and Josh Rector (Ace of Cloud) unpack the identity and access challenges posed by agentic AI. How do you verify it was the right agent, doing the right action, approved by the right person? How do we bound, constrain, govern agentic behavior? Ultimately, the same frameworks built for human identity and access should be applied to agents.
The Mythos Moment: Why the Future of Cybersecurity Is Software Trust
Anthropic’s Mythos announcement is not just another cybersecurity headline. It is a signal. AI is transforming software faster than security teams can adapt. The organizations that win won’t be the ones that simply find more flaws. They’ll be the ones that can prove their software can be trusted. A signal that software risk has entered a new era; one where AI can accelerate both the creation of software and the discovery of its weaknesses faster than human teams can respond.
The Exploit Window Collapse: Claude Mythos and the Future of Incident Response
Every so often, something comes along that forces you to recalibrate how you think about cyber risk. Not incrementally, but fundamentally. Claude Mythos feels like one of those moments. The cybersecurity industry has spent decades racing attackers to close vulnerabilities faster. Claude Mythos suggests that race may be entering an entirely new phase. One where speed itself becomes the defining risk factor.
A Critical Look at OpenClaw and NemoClaw
Surprise, surprise, agentic AI is advancing very quickly, and security isn’t quite keeping up. While most attention in recent times has focused on improving model capability, we’ve often been left wondering how to actually make these systems safe enough to trust with real-world tasks and limited interaction. This challenge has become particularly evident with the rise of platforms like OpenClaw, where autonomous agents can execute multi-step actions with minimal human oversight.
The CISO's AI Agent Production Approval Checklist: 7 Gates to Clear Before Go-Live
Your engineering lead is in your office Thursday morning. They want to push an AI agent to production next Tuesday. It’s a LangChain-based workflow agent, connected through MCP to three internal tools and one external API, with access to a customer database. The framework posters are on the wall. Your team has spent two quarters standing up runtime observability. And sitting in that chair, you still don’t know whether to say yes.
How to Triage an AI Agent Execution Graph: A Three-Tier Decision Framework for Security Teams
A platform security engineer gets an alert at 2:14 a.m. One of the LangChain agents running in their production Kubernetes cluster has produced an execution graph with eleven nodes, seven tool calls, and an egress edge to a domain that is not in the agent’s approved integration list. The chain is fully rendered in their console. Every signal is there.
AI Workload Baseline and Drift Detection: Defining "Normal" Agent Behavior
Security teams deploying AI agents into Kubernetes know they need behavioral baselines. The concept is straightforward: define what “normal” looks like for each agent, then detect when behavior drifts in ways that suggest compromise. The problem is that AI agents are designed to change. A model update alters inference latency. A prompt revision shifts tool-calling sequences. A new MCP integration adds API destinations nobody flagged during the last security review.
What is the OWASP Top 10 for LLM Application Security
Initially published by the Open Worldwide Application Security Project (OWASP) in 2023, the Top 10 for LLM Application Security list seeks to bridge the gap between traditional application security and the unique threats related to large language models (LLMs). Even where the vulnerabilities listed have the same names, the Top 10 for LLM Application Security focuses on how threat actors can exploit LLMs in new ways and potential remediation strategies that developers can implement.
Every Tech Revolution Follows This Pattern (AI Is No Different)
AI adoption is happening faster than any technology cycle in history. Information security and risk management are being sacrificed for speed and every single technology revolution has followed the same pattern. In this episode of Razorwire Raw, Jim Rees draws on decades of experience through the internet boom, virtualisation revolution and cloud computing adoption to explain what's actually happening with AI right now. Each cycle has been faster than the last, and each time, security gets left behind.