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 - 叶小钗
D
Docker
Google DeepMind News
Google DeepMind News
Y
Y Combinator Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Blog — PlanetScale
Blog — PlanetScale
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
U
Unit 42
博客园 - 【当耐特】
N
Netflix TechBlog - Medium
V
Visual Studio Blog
Microsoft Azure Blog
Microsoft Azure Blog
博客园_首页
Recent Announcements
Recent Announcements
GbyAI
GbyAI
T
Tailwind CSS Blog
S
SegmentFault 最新的问题
WordPress大学
WordPress大学
T
The Blog of Author Tim Ferriss
Engineering at Meta
Engineering at Meta
L
LangChain Blog
A
About on SuperTechFans
M
MIT News - Artificial intelligence
B
Blog

Help Net Security

Police arrest 10 suspected members of Black Axe cybercrime gang ShinyHunters claims it stole 1.4 million records from Udemy Sevii unveils Cyber Swarm Defense Mode to stop AI-driven attacks at scale Alleged Chinese hacker extradited to US over cyberattacks targeting COVID-19 research Cequence Agent Personas bring granular control and governance to enterprise AI agents NowSecure MARI gives enterprises evidence-based visibility into third-party mobile app risk The metrics killing your SOC, and what to use instead US state privacy fines reached $3.425 billion in 2025 Canada’s first SMS blaster case leads to three arrests Linux storage management tool Stratis 3.9.0 adds online encryption and cache-less pool startup TLS Connect gives SMBs a right-sized automated tool to manage TLS certificates Aptori expands its platform with autonomous offensive testing to reduce security bottlenecks Your IAM was built for humans, AI agents don’t care The AI criminal mastermind is already hiring on gig platforms 25 open-source cybersecurity tools that don’t care about your budget Product showcase: LuLu reveals unauthorized outbound connections from Mac apps Week in review: Claude Mythos finds 271 Firefox flaws, Vercel breach Users advised to drop passwords and make room for passkeys - Help Net Security Indirect prompt injection is taking hold in the wild - Help Net Security Compromised everyday devices power Chinese cyber espionage operations - Help Net Security New Cisco firewall malware can only be killed by pulling the plug - Help Net Security Meta is overhauling how you sign in, manage settings, and protect your accounts - Help Net Security Ubuntu 26.04 LTS delivers memory-safe system tools and live patching for Arm servers - Help Net Security OpenAI’s GPT-5.5 is out with expanded cybersecurity safeguards - Help Net Security AI is speeding up nation-state cyber programs - Help Net Security A study of 1,000 Android apps finds a privacy policy logging gap - Help Net Security IT spending to hit $6.31 trillion record, thanks to AI - Help Net Security Where AI in CI/CD is working for engineering teams - Help Net Security With AI's help, North Korean hackers stumbled into a near-undetectable attack - Help Net Security Hacker with a special interest in breaching sports institutions ends behind bars - 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.