#bigdatasecurityanalytics search results

This website is literally helps you Analyze Data with just prompts😳 It is an open-source AI Analyst that allows you: •Chat with your data warehouse •Generate SQL & Python code from questions •Auto-build Excel dashboards •Centralize metric definitions 🧵

ezekiel_aleke's tweet image. This website is literally helps you Analyze Data with just prompts😳

It is an open-source AI Analyst that allows you:

•Chat with your data warehouse
•Generate SQL & Python code from questions
•Auto-build Excel dashboards
•Centralize metric definitions

🧵

.@AlmanaxAI just released its latest cybersecurity evals! This is a comprehensive review of how different AI models compare at detection of code vulnerabilities. We also evaluated popular signature-based static analyzers. Here's what we found 👇🏽

francescpicc's tweet image. .@AlmanaxAI just released its latest cybersecurity evals!

This is a comprehensive review of how different AI models compare at detection of code vulnerabilities. We also evaluated popular signature-based static analyzers.

Here's what we found 👇🏽

Well... damn. New paper from Anthropic and UK AISI: A small number of samples can poison LLMs of any size. anthropic.com/research/small…

robertwiblin's tweet image. Well... damn.

New paper from Anthropic and UK AISI: A small number of samples can poison LLMs of any size.

anthropic.com/research/small…

We’ve conducted the largest investigation of data poisoning to date with @AnthropicAI and @turinginst. Our results show that as little as 250 malicious documents can be used to “poison” a language model, even as model size and training data grow 🧵

AISecurityInst's tweet image. We’ve conducted the largest investigation of data poisoning to date with @AnthropicAI and @turinginst. 

Our results show that as little as 250 malicious documents can be used to “poison” a language model, even as model size and training data grow 🧵

New research with the UK @AISecurityInst and the @turinginst: We found that just a few malicious documents can produce vulnerabilities in an LLM—regardless of the size of the model or its training data. Data-poisoning attacks might be more practical than previously believed.

AnthropicAI's tweet image. New research with the UK @AISecurityInst and the @turinginst: 

We found that just a few malicious documents can produce vulnerabilities in an LLM—regardless of the size of the model or its training data.

Data-poisoning attacks might be more practical than previously believed.
AnthropicAI's tweet image. New research with the UK @AISecurityInst and the @turinginst: 

We found that just a few malicious documents can produce vulnerabilities in an LLM—regardless of the size of the model or its training data.

Data-poisoning attacks might be more practical than previously believed.

❄️ Using Snowflake? You might be sitting on hidden data risk. Get a free data risk assessment from BigID, plus a custom sample report tailored to your Snowflake setup. Discover what’s hiding, close gaps, and stay secure. Start now 👉 bit.ly/4kBCw1b #Snowflake

bigidsecure's tweet image. ❄️ Using Snowflake? You might be sitting on hidden data risk.

Get a free data risk assessment from BigID, plus a custom sample report tailored to your Snowflake setup. Discover what’s hiding, close gaps, and stay secure.

Start now 👉 bit.ly/4kBCw1b

#Snowflake…

So tempted to just sell $BBAI and take $680 profit after one month…

SavageDiscavage's tweet image. So tempted to just sell $BBAI and take $680 profit after one month…

the tools to make research like a pro (better than 98%) @glassnode: on-chain market insights @dappradar: web3 projects aggregator @defillama: largest data aggregator for DeFi @arkham: on-chain analytics platform @dune: crypto's data hub (dashboards) @artemis: on-chain…

DeRonin_'s tweet image. the tools to make research like a pro (better than 98%)

@glassnode: on-chain market insights
@dappradar: web3 projects aggregator
@defillama: largest data aggregator for DeFi
@arkham: on-chain analytics platform
@dune: crypto's data hub (dashboards)
@artemis: on-chain…

$BBAI Shares rose after the company partnered with Tsecond to develop AI-powered edge hardware for real-time threat detection in national security. Target price: $10.50 If defense contracts accelerate and AI deployment is successful, Target price could reach $20.

mitchellakaplan's tweet image. $BBAI

Shares rose after the company partnered with Tsecond to develop AI-powered edge hardware for real-time threat detection in national security.

Target price: $10.50

If defense contracts accelerate and AI deployment is successful,

Target price could reach $20.
mitchellakaplan's tweet image. $BBAI

Shares rose after the company partnered with Tsecond to develop AI-powered edge hardware for real-time threat detection in national security.

Target price: $10.50

If defense contracts accelerate and AI deployment is successful,

Target price could reach $20.

Data is everywhere. AI is everywhere. And with it risk is everywhere. Most teams struggle to keep up. Fragmented tools, blind spots, and silos leave gaps that put sensitive data at risk. BigID changes that: one platform to know your data, control your risk, and govern with…


.@neutrl_labs is switching on proactive protection in a partnership with Hypernative 🤝 🔹Continuous threat monitoring 🔹High-accuracy alerts 🔹Automated response to threats 🔹Transaction simulation & policy-based enforcement 🔹And more Read more: buff.ly/5FXZ5a3

HypernativeLabs's tweet image. .@neutrl_labs is switching on proactive protection in a partnership with Hypernative 🤝 

🔹Continuous threat monitoring
🔹High-accuracy alerts
🔹Automated response to threats
🔹Transaction simulation & policy-based enforcement
🔹And more 

Read more: buff.ly/5FXZ5a3

New @AISecurityInst research with @AnthropicAI + @turinginst: The number of samples needed to backdoor poison LLMs stays nearly CONSTANT as models scale. With 500 samples, we insert backdoors in LLMs from 600m to 13b params, even as data scaled 20x.🧵/11

AlexandraSouly's tweet image. New @AISecurityInst research with @AnthropicAI + @turinginst:
The number of samples needed to backdoor poison LLMs stays nearly CONSTANT as models scale. With 500 samples, we insert backdoors in LLMs from 600m to 13b params, even as data scaled 20x.🧵/11

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