AI

AI-powered cyber threat intelligence analysis tool

Hacker News

AI-powered cyber threat intelligence analysis tool

Hi HN, I am Ted. I've been on both sides of the security fence – working security operations for clients and building enterprise security tools for vendors. One thing I've learned from years of hands-on experience is that fast and accurate information is king for good threat analysis. The problem is, it can be a real drag. You often have to juggle 5-7 different tools and webpages just to investigate a single threat case. This whole manual process of gathering info, piecing it together, and figuring out what it all means doesn't pay off like other security tasks. Then came along these cool new GenAI models. They are amazing at planning tasks, finding information, and putting it all together. It hit me – this could be a game-changer for threat analysis. Over the past year, I've seen big names like Microsoft, Crowdstrike, and Palo Alto have integrated AI capabilities into their threat analysis offerings. But these tools are expensive and often lock you into their own little data worlds. That's why I built SecAI. Think of it as your open, easy-to-use, and totally affordable AI assistant for security analysis. SecAI is aiming to empowers security analysts to achieve greater efficiency and productivity. Here's what SecAI brings to the table: 1.Threat intelligence analysis and attribution with precise verdicts and comprehensive context. 2.Analyzing threat behavior from logs and scripts, providing insights. 3.Tracking threat actors/CVEs across the entire internet and generating detailed reports. Please check it out and let me know what you think. I would be grateful for your feedbacks. https://secai.ai/chat

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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