Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

protecto

Agentic Data Classification: A New Architecture for Modern Data Protection

Jan 30, 2026 By Amar Kanagaraj In Protecto
In the evolving landscape of data protection and compliance, data classification is the bedrock of safe AI workflows. Yet legacy approaches rely on singular models that are fixed, rigid, and limited in context. Our agentic data classification approach reshapes this paradigm by not relying on any single model. Instead, we orchestrate a dynamic, intelligent layer that automatically selects the right model for the job.
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AI for Security Infrastructure: Rebalancing Cybersecurity for the Decade Ahead

AI for Security Infrastructure: Rebalancing Cybersecurity for the Decade Ahead

Jan 29, 2026 By Garrett Hamilton, CEO and Co-Founder In Reach Security
For more than a decade, cybersecurity has been shaped by a single doctrine: assume breach. Facing high-volume, relentless, and diverse attacks, the security industry has been forced into a reactive stance, playing a constant game of whack-a-mole in a nonstop damage-limitation exercise. This has driven major investment in detection, response, and recovery, and created a world in which organizations are better at reacting to incidents than at preventing them in the first place.
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nightfall

Beyond Pattern Matching: How AI-Native File Classification Solves Modern DLP Challenges

Jan 29, 2026 By Chris Martinez In Nightfall
Legacy DLP operates on a fundamental constraint: it identifies sensitive data by matching patterns. Credit card numbers follow the Luhn algorithm. Social Security numbers conform to a nine-digit format. API keys match specific string patterns. This approach works for structured data, but it fails to address a critical reality: Your most sensitive assets aren't numbers. They're documents.
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Forward Networks

Introducing Forward AI

Jan 29, 2026 By Forward Networks In Forward Networks
As enterprises move toward agentic operations, speed without data accuracy becomes a liability. At Forward Networks, we recognized this challenge and set out to deliver a solution: speed backed by mathematical accuracy. In networking, acting on incomplete or approximate data is not an inconvenience, it is a cause of outages, security exposure, and operational risk.
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AI is Actively LEAKING Your Data (And You Don't Know It) #apisecurity #airisks #dataprotection #ai

Jan 29, 2026 By Wallarm In Wallarm
AI agents don't think. They pattern-match. Critical to understand: Generative AI (ChatGPT, Claude, etc.) does NOT reason like humans. It: The API Security problem: When you give an AI agent access to an API, it will: AI agents can't reason. They recreate patterns based on weights. You need to be very careful: data in, data out. Practical example: text User: "Show me the account balance for user" AI agent → calls GET /api/account/123 API → returns { balance: 5000, name: "John", SSN: "123-45-6789" } AI agent → outputs EVERYTHING to user (including SSN!)
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Introducing Forward AI

Jan 29, 2026 By Forward Networks In Forward Networks
The Network is Complex. Operating It Shouldn't Be. Forward AI transforms network operations by reducing manual analysis, expert dependency, and guesswork. By combining conversational interaction with a mathematically accurate digital twin, teams can validate intent, understand actual network behavior, and act with confidence across even the most complex environments.
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gitguardian

OpenClaw (Moltbot) Personal Assistant Goes Viral - And So Do Your Secrets

Jan 29, 2026 By Guillaume Valadon In GitGuardian
Early 2026, Moltbot a new AI personal assistant went viral. GitGuardian detected 200+ leaked secrets related to it, including from healthcare and fintech companies. Our contribution to Moltbot: a skill that turns secret scanning into a conversational prompt, letting users ask "is this safe?".
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LimaCharlie

Threat hunting to detection engineering: Analyzing real malware with Claude Code, LimaCharlie, and Linux

Jan 29, 2026 By LimaCharlie In LimaCharlie
Claude Code, originally just auto-complete on steroids for IDEs, shows a lot of promise for becoming a major tool in the DFIR/detection engineering/security analyst’s toolbox. Whether it’s Claude Code’s support of MCP, agent skills, or general ability to quickly figure out how to accomplish a given task, it is rapidly becoming more than a code generation tool. This is the first of a three-part series.
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