Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

When AI changes the rules, attackers adapt

The dominant narrative around AI in security is one of emboldened defenders suppressing attackers. Yet, not everyone is convinced the future will be so rosy. In a recent Defender Fridays episode, Josh Neil, Co-founder and CTO of Alpha Level, made an argument that cuts against the celebratory mood: as AI makes known attack vectors harder to use, adversaries don't disappear. They adapt. For MSSPs and SOC teams, an adversary that looks like a user is a harder problem than one that looks like malware.

Ep 44: You can't vibe code your way through a production outage

In this episode of Masters of Data, we tackle one of tech's buzziest debates: vibe coding versus production-ready software. We break down where AI-assisted "just make it work" coding genuinely shines (think POCs, prototypes, and getting stakeholder buy-in fast) and where it falls dangerously short when someone tries to ship it to ten thousand enterprise users. We also dig into David's agentic engineering workflow, security risks like malicious MCP servers and supply chain attacks, and why turning a vibe-coded prototype into real software still takes months, not days. Bottom line.

How an AI SEO Agency Helps SaaS Businesses Rank Faster Online

Software companies often depend on search visibility long before paid acquisition becomes efficient. Yet many teams publish pages without a clear intent map, a crawl plan, or realistic ranking priorities. Results slow down for predictable reasons. Search growth usually improves when technical repair, keyword research, and content planning move in the right order. With that structure in place, SaaS brands can reach evaluators earlier, support longer buying cycles, and build a steadier pipeline from organic discovery.

Invisible Cross-Tracking: How Mobile Apps Share Your Data and How to Stop It

Tracking user activity across apps on mobile devices is crucial, as data no longer flows from a single source on phones. For example, in the span of an hour, a user might open Instagram, Gmail, a shopping app, a weather app, and a free game, while various advertising tools quietly analyze network signals, device behavior, location data, and app usage patterns. A VPN won't remove every unique identifier in these apps, but it does make it harder to connect one link in this tracking chain: the digital network footprint.

DLP for GenAI: How to Prevent Sensitive Data Leaks in AI Tools

Employees are feeding sensitive data into AI tools at a pace most security teams did not anticipate. Source code goes into coding assistants. Customer records get pasted into ChatGPT to draft emails. Confidential contracts land in Gemini for summarization. According to Cyberhaven Labs research, 39.7% of the data employees share with AI tools is sensitive, and the volume is accelerating as AI adoption spreads from individual contributors to entire workflows.

Can Existing CNAPPs Secure AI Agents in Cloud Environments? Where Each Domain Stops

A CNAPP isn’t a single instrument. It bundles five separately-instrumented security domains — CSPM, CWPP, CIEM, CDR, and a fifth add-on module marketed as AI security — each watching a different observation point. So when leadership asks whether your CNAPP can secure the AI agents your team has shipped, you don’t get one answer. You get five.

AI Agent Governance: From Policy Framework to Runtime Enforcement

Most enterprise AI agent governance programs publish policies at the bottom three rungs of a runtime enforceability ladder while their architecture diagrams claim rung four. Almost no program reaches rung five, the only rung that produces evidence an auditor cannot dispute. The mismatch shows up in the audit committee meeting. The CISO walks in with the NIST AI RMF mapping, the AUP, the model cards, and the vendor risk assessments for every third-party API the agents call.

Why 'Secure' Mobile Apps Still Get Hacked | Post-Deployment Security

Your app passed testing. CI/CD ran clean. The App Store approved it. Your security team signed off. Six weeks later, attackers are reverse-engineering the binary on rooted devices, injecting JavaScript into your runtime, and probing API endpoints your scanner never modeled. Nothing in the code changed. The threat environment did. This is the central problem of modern mobile application security, and it doesn't get fixed by adding more pre-release scanners.