惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 司徒正美
T
The Blog of Author Tim Ferriss
F
Fortinet All Blogs
Martin Fowler
Martin Fowler
罗磊的独立博客
The GitHub Blog
The GitHub Blog
L
LangChain Blog
A
About on SuperTechFans
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
D
DataBreaches.Net
宝玉的分享
宝玉的分享
U
Unit 42
阮一峰的网络日志
阮一峰的网络日志
Last Week in AI
Last Week in AI
N
Netflix TechBlog - Medium
The Cloudflare Blog
Microsoft Azure Blog
Microsoft Azure Blog
H
Help Net Security
美团技术团队
大猫的无限游戏
大猫的无限游戏
雷峰网
雷峰网
爱范儿
爱范儿
酷 壳 – CoolShell
酷 壳 – CoolShell
MongoDB | Blog
MongoDB | Blog

Help Net Security

ChatGPT advanced account security adds passkeys and hardware keys Week in review: High-severity LPE vulnerability in the Linux kernel, cPanel 0-day exploited for months Automating Pentest Delivery: A Step-by-Step Guide - PlexTrac Open-source privacy proxy masks PII before prompts reach external AI services Shadow AI risks deepen as 31% of users get no employer training Identity is the control plane for distributed infrastructure AI traffic is getting bigger, louder, and less predictable New infosec products of the month: April 2026 cPanel zero-day exploited for months before patch release (CVE-2026-41940) Cisco releases open-source toolkit for verifying AI model lineage Met Police face criticism for using AI to spy on their own officers Nine-year-old Linux kernel flaw enables reliable local privilege escalation (CVE-2026-31431) Hacker with a special interest in breaching sports institutions ends behind bars - Help Net Security IP Fabric MCP server adds governance and control to enterprise AIOps workflows - Help Net Security Aqua Compass MCP server enables real-time investigation and containment of runtime threats - Help Net Security Google brings instant email verification to Android, no OTP needed - Help Net Security If cyber espionage via HDMI worries you, NCSC built a device to stop it - Help Net Security Apple fixes iPhone bug that let FBI retrieve deleted Signal messages(CVE-2026-28950) - Help Net Security GopherWhisper APT group hides command and control traffic in Slack and Discord - Help Net Security OpenAI tackles a bad habit people have when interacting with AI - Help Net Security A year in, Zoom's CISO reflects on balancing security and business - Help Net Security Scenario: Open-source framework for automated AI app red-teaming - Help Net Security GDPR works, but only where someone enforces it - Help Net Security Ransomware, fraud, and lawsuits drive cyber insurance claims to new peaks - Help Net Security Google’s Workspace Intelligence promises privacy while running on your data - Help Net Security Cyberattack on French government agency triggers phishing alert - Help Net Security Claude Mythos finds 271 Firefox flaws, Mozilla believes zero-days are numbered - Help Net Security Prove Identity Platform connects verification, authentication, and fraud prevention - Help Net Security New Mirai variants target routers and DVRs in parallel campaigns - Help Net Security Acronis GenAI Protection gives MSPs control over AI usage and data risks - Help Net Security
Seemplicity AI Analysts focus remediation on exploitable ...
Industry News · 2026-06-25 · via Help Net Security

Seemplicity has launched AI Analysts for exposure management and response. The autonomous agents replace manual vulnerability triage by working directly within remediation workflows to conduct structured, evidence-based exploitability investigations.

Seemplicity AI Analysts

The old playbook is broken. AI-generated exploits have collapsed the window between disclosure and weaponization from days to hours, and the static scores and external signals teams once trusted can no longer keep pace.

“The danger was never the alerts we could see, it was the handful of genuinely exploitable ones buried among them,” said Michael Varicak, manager of vulnerability management for Omnissa. “Seemplicity’s AI Analysts surface exactly that with the context to prove it, so we spend our time fixing what attackers could actually exploit.”

The hard part isn’t ranking findings, it’s confirming which ones an attacker could actually reach. The Seemplicity AI Analysts answer that by investigating each asset in real time, analyzing live runtime configurations, exploit prerequisites, network reachability, code and dependency usage, and compensating controls. The result is a dramatically smaller, more actionable list of what needs immediate response, backed by transparent evidence.

“Zero-day discovery at this scale pushes us out of today’s CVE disclosure process and instigates a need to reindustrialize,” said Erik Nost, senior analyst, security and risk at Forrester. “Patch Tuesday will no longer be marked on the calendar: A 30-day waiting period for patching won’t be acceptable in an environment where attackers can go from discovery to exploit in minutes.”

In preliminary deployments, the Seemplicity AI Analysts found that the majority of high-severity findings were not actually exploitable, cutting alert noise and pointing teams straight at the exposures that carry real-world risk.

“We’re not giving security teams another number to chase. We’re giving them the evidence to act,” said Ravid Circus, Chief Product Officer at Seemplicity. “A score can’t tell you whether a vulnerability is actually exploitable on your systems. Only context can. Our AI Analysts are unique by investigating the asset itself, the runtime conditions, the network exposure, and the code to prove what an attacker could really reach.”

Seemplicity AI Analysts serve three core functions:

The Host Analyst: Confirms whether a host vulnerability is truly exploitable by examining live runtime configuration, exploit prerequisites, and network reachability, then surfaces the likely owner to speed the fix.

The Code Analyst: Reads source directly from GitHub, GitLab, and other connected repositories to determine whether vulnerable code is actually reachable, then hands developers a scoped, ready-to-apply fix.

The SCA Analyst: Confirms which dependencies are genuinely used and reachable across the software supply chain, and powers SBOM-driven zero-day response before scanner signatures even exist.

How AI Analysts work together

Evidence over assumptions. Every verdict comes from a structured investigation of runtime context, threat intelligence, network exposure, and code, not a static risk score.

Clear chain of reasoning. Each finding carries an auditable trail of how the verdict was reached, so security and engineering teams agree on what to fix.

Built into remediation workflows. Verdicts and fixes land inside findings tables, workflow automations, and ticketing systems, with no separate validation tool or console required.