Alert overload is one of the hardest ongoing challenges for a Tier 1 SOC analyst. Every day brings hundreds, sometimes thousands of alerts waiting to be triaged, categorized, and escalated. Many of them are false positives, duplicates, or low-value notifications that muddy the signal.  

When the queue never stops growing, even experienced analysts start losing clarity, missing patterns, and risking oversight of critical threats.

Beyond Burnout: How Alert Fatigue Destroys Careers 

Alert overload isn’t just unproductive — it’s toxic. Constant false positives create chronic stress, anxiety, and decision fatigue. Analysts doubt themselves, experience imposter syndrome, and burn out fast. Many leave the industry within years, citing mental health tolls like sleep loss and eroded confidence from missing “the big one” amid the chaos. 

Tier 1 analysts who triage efficiently using context gain sharp investigation skills, earn trust for escalations, and accelerate to Tier 2/3 roles. They avoid burnout, stay passionate about cybersecurity, and position themselves as indispensable experts in a high-demand field. Solutions like ANY.RUN’s Threat Intelligence Lookup can provide a master key not only to an analyst’s career, but to the next level of SOC efficiency.

Cutting Through the Chaos: How Threat Intelligence Keeps Analysts Effective 

Alert overload at Tier 1 creates bottlenecks: unnecessary escalations flood senior analysts, response times balloon, and real breaches slip through. This drains budgets on prolonged incidents, erodes team morale, and weakens organizational defenses, turning a proactive SOC into a reactive firefighting unit. 
 
Threat intelligence gives analysts the missing piece they often need during triage: context. Instead of manually searching for data across multiple sources, TI instantly tells you what the alert is truly about. 

Was this domain seen in phishing attacks? Is this hash connected to a malware family? Is the mutex associated with known malicious samples? 

With enriched data, Tier 1 analysts spend less time guessing and more time making confident decisions. Context transforms alerts from ambiguous into actionable and significantly reduces both cognitive load and triage time. 

The key is having threat intelligence that’s immediately accessible during your investigation workflow, comprehensive enough to cover the indicators you encounter, and current enough to reflect the latest threat landscape. When used effectively, threat intelligence doesn’t just help you process alerts faster. It improves your accuracy, reduces the anxiety of uncertainty, and helps you develop the threat intuition that distinguishes experienced analysts.

Context on Demand: Understand an Alert Fast

ANY.RUN’s Threat Intelligence Lookup provides immediate, precise context from one of the largest ecosystems of analyst-generated data worldwide. It connects information from 15,000+ SOCs and security teams and presents it in a clean, friendly format. 

Instead of digging through scattered reports, teams get immediate answers: malware classification, sample behavior, network connections, relationships, and IOCs — all based on real sandbox runs. 

This dramatically shortens triage time and reduces the chance of overlooking critical details hidden inside the noise. 

Real-World Wins: See TI Lookup in Action 

From Vague Domain to Clear Verdict 

An alert flags a weird domain in network traffic. Paste it into ANY.RUN TI Lookup: instantly reveal if it’s a known C2 server, tied to ransomware like LockBit, with resolved IPs, associated hashes, and full attack chains from recent sandbox runs. Result? Confident closure or escalation, saving hours and stopping lateral movement cold. 

domainName:”edurestunningcrackyow.fun” 

How To Make a Hash Talk

EDR alerts on a dropped executable hash. Query TI Lookup: uncover the exact malware family (e.g., RedLine stealer), prevalence stats, extraction TTPs, and behavioral details from detonations. Benefit: Precise containment (block similar hashes), updated detections, and proof for stakeholders: no deep dives needed. 

md5:”dfe60536382cc0d30416bce4c85e6044″ 

Mutex Magic: Unmask Persistent Threats Fast

A process creates an odd mutex (mutual exclusion object). Search it in TI Lookup’s synchronizations tab: link it to families like DCRat or AsyncRAT, view creating processes, and jump to sandbox sessions showing persistence tactics. Outcome: Rapid hunting across endpoints, stronger YARA rules, and blocking reinfection before damage spreads. 
syncObjectName:”*sm0:4360:304:wilstaging_02″ 

Stop Surviving Alerts. Start Dominating Them

Alert overload is not an inevitable curse of SOC work, it’s a solvable problem that demands both systemic improvements and individual strategy.  

The difference between analysts who burn out and those who thrive often comes down to their ability to extract context quickly, make confident decisions, and focus their limited time on high-value investigations. Threat intelligence platforms like ANY.RUN’s Threat Intelligence Lookup are not magic solutions that eliminate alerts, but they are force multipliers that transform your effectiveness by providing the context that turns ambiguous indicators into clear decisions.  

By integrating threat intelligence into your daily workflow, you reduce investigation times from minutes to seconds, improve accuracy by relying on aggregated community knowledge, and build the pattern recognition skills that define senior analysts. The critical incidents hiding in your alert queue will only become visible when you clear away the noise efficiently enough to spot them.  

Take control of your alerts before they control you, leverage the intelligence resources available to you, and remember that becoming a great analyst isn’t about handling every alert. It’s about handling the right alerts in the right way.

FAQ 

About ANY.RUN  

ANY.RUN is a leading provider of interactive malware analysis and threat intelligence solutions.  Today, 15,000+ organizations worldwide use ANY.RUN to speed up investigations, strengthen detection pipelines, and give their teams a clearer view of what’s really happening on their endpoints.  

SOC teams using ANY.RUN report measurable improvements, including:  

• 3× boost in SOC efficiency; 

• 95% faster initial triage; 

• Up to 58% more threats identified; 

• 21-minute reduction in MTTR per incident. 

Start your 14-day trial of ANY.RUN today →    

The post How to See Critical Incidents in Alert Overload: A Guide for SOCs and MSSPs  appeared first on ANY.RUN’s Cybersecurity Blog.

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In many SOCs, phishing analysis still follows the same old pattern: manually pull apart URLs, inspect attachments by hand, take screenshots, collect indicators one by one… and hope nothing slips through in the process. 
It’s careful work, but slow. 

A sandbox flips that workflow on its head. 

Every step analysts normally handle themselves is condensed into a few seconds of automated detonation, real-time behavior tracking, and instant IOC extraction. 
That’s how a 15-minute job becomes a 60-second answer. 

How Phishing Analysis Really Works: With vs. Without a Sandbox 

Once the email is flagged as suspicious, analysts usually move through a predictable checklist: review the link or attachment, open it inside a safe environment, observe what happens, and extract indicators manually. Each phase takes time, and even small tasks, decoding a URL, grabbing a screenshot, checking a redirect; slow the investigation down. 

When the same message is detonated inside ANY.RUN sandbox, the whole chain is captured automatically. The VM loads the content, follows redirects in real time, records every network request, and pulls out indicators as soon as the activity appears. Instead of digging piece by piece, analysts simply watch the behavior unfold and confirm the verdict. 

A good example of this speed is a recent phishing case where attackers used Figma pages to kick off a credential-harvesting chain. Inside the sandbox, the entire Figma → Microsoft microdomain → Azure Blob Storage flow becomes visible in under a minute. 

You can view the attack pattern here: Real Example: FigmaRedirects Used in Phishing 

1. URL / Attachment Analysis 

Without a sandbox: 
Analysts usually begin by checking links manually, opening attachments in a VM environment, or trying to reproduce the user’s click path. Even simple emails take time to decode and verify, often adding up to 5–10 minutes before any real behavior is observed. 

With a sandbox: 
Upload the email or attachment, and the sandbox detonates it instantly inside a controlled VM. Initial behavior, connections, redirects, script execution, appears in 20–40 seconds, giving analysts a fast idea of whether the file is benign or dangerous. 

2. Behavior Observation 

Without a sandbox: 
Once the link or attachment is opened in a controlled VM, analysts have to follow the behavior manually; redirects, process launches, hidden scripts, background network calls. None of it is being tracked in a custom VM deployed locally by default. 

The workflow is slow because everything happens in small pieces that need to be captured one by one. Depending on the complexity of the email, this phase can take anywhere from several minutes to well over ten, especially if the chain includes multiple hops or short-lived activity. 

With a sandbox: 

The moment the detonation starts, the sandbox records each action as it happens. 
Processes, redirects, and network requests appear live in the interface, so analysts see the full flow without chasing events across different tools. In most cases, the main behavior is already visible within 20–40 seconds, including activity that would be easy to miss during observation in a custom VM. 

3. IOC Extraction 

Without a sandbox: 
Collecting indicators is usually one of the most time-consuming parts of phishing analysis. Analysts have to pull out every domain, IP address, hash, and dropped file path manually, sometimes by repeating the execution to catch fast or hidden activity. Cross-checking each indicator across logs, browsers, and tools can stretch this phase to 5–10 minutes or more, especially when the redirect chain is long. 

With a sandbox: 

Indicators appear as soon as the activity occurs. Domains, IPs, file hashes, registry changes, and dropped objects are captured automatically and displayed in a single view. Instead of hunting for details, analysts simply review the list. This typically takes 10–20 seconds, even when multiple indicators are created during detonation. 

4. Threat Matching 

Without a sandbox: 
After gathering indicators, analysts typically check each domain, IP, and file hash in external reputation portals or TI sources. Moving between tools and validating each indicator one by one often adds 5–10 minutes, especially when the phishing chain produces several IOCs. 

With a sandbox: 
Reputation details appear automatically as soon as indicators show up. The ANY.RUN sandbox displays the name of the threat, whether it is a malware family, a phishing kit, or even an APT. The threat coverage is continuously updated by ANY.RUN’s in-house team of threat hunters, researchers, and analysts. 

Suspicious findings are also labeled with clickable threat names, allowing analysts to jump directly to related public submissions for deeper comparison. Besides, a link to the Malware Trends Tracker provides broader context, showing how the threat behaves across other samples. 

What normally requires several manual lookups takes 10–20 seconds, because the essential context is already available in the interface. 

5. Incident Documentation 

Without a sandbox: 
Documenting findings is one of the most tedious parts of phishing analysis. 
Analysts need to capture screenshots, save URLs, gather indicators, describe behavior, and assemble everything into a ticket or report by hand. Even when the case is simple, this often requires 5–10 minutes, and much longer when multiple steps or redirects are involved. 

With a sandbox: 
A complete report is generated automatically as the detonation runs. Screenshots, network activity, redirects, process events, indicators, and threat labels are all captured and stored in a structured format. 

Analysts can export the report instantly or link directly to it, so the case can move forward without manual writing or screenshot collection. This entire phase usually takes 10–20 seconds, since the documentation is created for you. 

The Time Difference: 15 Minutes vs. 60 Seconds 

When you put each step side by side, the gap becomes obvious. 
Manual phishing analysis breaks the workflow into several slow checks, while an interactive sandbox condenses everything into one fast detonation. 

What’s usually a long, repetitive workflow turns into a one-minute verdict. 
When a phishing wave hits and dozens of suspicious emails land in the queue, those saved minutes quickly add up, often freeing hours across a single shift. 

Why This Speed Counts: The Business Impact Behind the Numbers 

Cutting phishing analysis from 15 minutes to 60 seconds drives measurable improvements across the entire SOC. 

ANY.RUN’s data shows: 

• 90% of malicious activity is exposed within the first 60 seconds of detonation 
  → analysts see the real behavior before the attacker has time to hide it 

• 94% of users report faster triage 
  → fewer tasks stuck in the queue, fewer delays during active phishing waves 

• SOCs that adopt interactive sandboxing see up to a 3× boost in investigation throughput 
  → more cases closed per shift, less pile-up when suspicious emails spike 

• False-positive noise drops significantly 
  → threat analysts spend more time on real signals and less on dead ends 

• Teams report up to a 58% increase in threats identified overall, including attacks that bypass other controls 
  → behavioral visibility picks up what static checks miss 

This combination, fast verdicts, clear behavior visibility, and automated context, transforms phishing analysis from a slow manual chore into a fast, reliable, repeatable process. 

Want to see how your SOC can speed up phishing investigations? 

Discover how interactive analysis cuts investigation time and exposes phishing behavior in under a minute. 

Get a 14-Day Trial of ANY.RUN → 

About ANY.RUN 

ANY.RUN helps security teams investigate threats faster and with far greater clarity. The Interactive Sandbox reveals full attack behavior in real time, from process execution and redirects to network activity and dropped files, giving analysts the visibility they need to make confident, evidence-based decisions. 

Cloud-based and ready to use, ANY.RUN supports Windows, Linux, and Android environments, making it easy to analyze phishing emails, URLs, and malware without managing complex infrastructure. 

Its Threat Intelligence Lookup and continuously updated TI Feeds provide automation-ready indicators that strengthen detection, enrichment, response, and reporting across security operations. 

Together, these capabilities give analysts a fast, transparent, and reliable way to understand modern attacks and improve overall SOC performance. 

The post Detected in 60 Seconds: How to Identify Phishing with a Malware Sandbox  appeared first on ANY.RUN’s Cybersecurity Blog.

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Black Friday is an annual bargain hunt that often spirals into chaotic impulse buying. Stores promise incredible discounts of 50–70%, but are those savings really as significant as they seem? In 2025, we’ve got a new ally on our side in the fight for smart spending: artificial intelligence. Here’s how you can use powerful LLMs like ChatGPT and Claude to save money and never fall for a shady seller’s tricks again.

Before we enlist AI to help you save, it’s crucial we understand the battlefield. Studies paint a grim picture: a significant portion of those Black Friday “super discounts” are nothing more than a marketing illusion.

The tactic is simple and effective: in early October, stores hike up their prices, sometimes by fifty to a hundred percent. Then, when Black Friday finally hits, they “slash” the price by that same 50% and proudly tout the impressive discount on the tag. In reality, you’re just buying the item at its regular price — or sometimes even paying a premium.

While the European Union’s Omnibus Directive mandates that retailers display the lowest price from the last 30 days, even this rule is easily skirted. Retailers just hike the price up 30 days before the event, which allows them to technically adhere to the directive while still duping consumers.

How LLMs can help you save

Artificial intelligence is changing the game. Analysts estimate that in 2024, AI tools helped consumers make a staggering $60 billion in transactions during Cyber Week, and that number is only projected to climb in 2025. Already, one in three U.S. shoppers plans to lean on AI for their shopping needs.

As you know, an LLM is immune to emotion; it won’t react to marketing triggers like “2 hours left!” or “only one left in stock!” Instead, the model analyzes huge volumes of data, compares prices, tracks price history, and helps you make rational decisions.

In seconds, AI can crawl hundreds of online stores, zeroing in not only on the product you want at the lowest price but also on cheaper alternatives with comparable specs. Modern LLMs can help you figure out if a discount is truly beneficial — or if you’re falling for a scam. Amazon, for example, has already integrated a price-tracking feature into its AI assistant, Rufus, though users have noted that the tool still has some kinks to work out. Using just a few prompts, the AI can factor in your preferences, budget, and past purchases to suggest exactly what you need, cutting through all the marketing noise. Instead of wasting hours poring over spec sheets, just ask the assistant, “What’s the difference between vacuum cleaner A and vacuum cleaner B?” And you get your answer — regardless of whether the seller’s website features a comparison tool. You can use the prompts below for ChatGPT, Claude, or Gemini.

Preparing for Black Friday with AI

Step 1. Create a wish list

Don’t wait for the sales to start; your goal is to gather all the baseline data upfront.

Help me create a shopping list for Black Friday. My budget is: [amount].
I'm interested in the following categories: [electronics/clothing/home goods].
Priorities: [performance/quality/brand/price].
Create a structured list with explanations of why each item is worth considering.

Step 2. Start tracking prices

This is a critical stage. You need to know the real price of an item before the Black Friday marketing hype machine starts rolling. On Amazon, tools like CamelCamelCamel and Keepa can help, and for AliExpress, look at AliPrice and AliTools.

Step 3. Analyze price dynamics

Collected the price data? Excellent. If you see a sharp price spike in October followed by a corresponding drop in November, you’re looking at the classic scam tactic. But if the data on the charts seems unclear, use the prompt below. The months we used are just examples, so feel free to use your own date ranges. The larger the intervals between the price checks, the higher your chances of catching an unjustified price hike.

I'm tracking [product name] on [platform]. Here's the price data:
- September: [price]
- Early October: [price]
- Late October: [price]
- Current price: [price]
- Advertised discount: [percentage]
- Analyze this data. Is this a genuine discount or is the store manipulating prices?
When is the best time to buy? Should I wait for Black Friday or buy now?

Step 4. Search for alternatives

Don’t get fixated on a single product. There may be more advantageous alternatives available.

I want to buy [product, model]. My goal is to [what it's needed for]. Budget: [amount].
Find 3–5 alternative products that solve the same problem but might be more cost-effective.
Compare them based on features, price, and reviews. Display the results in a table.

Experience shows that LLM models are particularly good at comparative analysis, highlighting key differences between similar products.

Step 5. Vet the seller and the website

Black Friday is an absolute field day for scammers. In the third quarter of 2025 we saw the number of fake online stores skyrocket by 20% compared to the monthly average. Let’s run through the immediate red flags that should raise your suspicions:

• Domains like .shop, .store, .vip or .top — rarely used by major, established brands
• Unbelievable discounts of 80–90% on popular items
• Lack of a secure HTTPS connection, meaning no padlock icon next to the URL in your browser
• Poorly translated text and/or grammatical errors

Finally, just in case, run the following prompt through the AI of your choice to check the store’s legitimacy:

I have found [product name] on [URL]. The price is very attractive: [price], which is [percentage]% below the average. How can I verify that this is not a scam?
What are the signs of a fake store? What should I pay attention to?

Step 6. Compile the all-in-one prompt

This is the all-in-one prompt containing all the data you gathered in the previous steps; it works in any LLM:

You are an expert in spotting retail price manipulation.
Product: [name]
Store: [name]
Current price: [price]
Advertised discount: [percentage]%
Stated old price [price]
Price history I tracked:
[state data for several months]
Tasks:
1. Is this a genuine discount or a manipulation?
2. What was the real average price before the alleged sale?
3. Should I buy now, or is the price likely to drop even further?
4. Your verdict: buy / wait / look for alternatives?

Note that neural networks’ cybersecurity is still far from perfect: vulnerabilities continue to be discovered within them. Therefore, to shield yourself from phishing and spam links you might accidentally follow, be sure to install a proven and reliable security solution, such as Kaspersky Premium. It’ll keep your Black Friday from turning into a financial Black Monday for both your assets and personal data.

Getting local results

One of the core issues with global AI models is that they often deliver information that’s not region-specific, or is relevant to a region other than yours. But you can adapt them to your needs with this prompt:

You are an AI shopping assistant for [country, city]. All your recommendations must factor in the local market, available stores, and regional platforms ([list of stores, if desired]). State prices in [currency]. Speak [language].
My task is to find [product] at the best price for Black Friday.
Which local platforms should I check? What kind of sales are common in [region]?

Specialized prompts for each LLM

Each LLM has its strengths (also weaknesses). With these in mind, we’ve created prompts that unlock the potential of each language model. For the highest quality results, we recommend utilizing models with a larger number of parameters (usually available via paid subscriptions), and activating deep thinking when submitting your requests.

ChatGPT excels at structuring information and generating lists. Here’s a prompt for budget planning:

Create a shopping strategy for Black Friday.
Budget: [amount]
Priority categories: [list]
For each category, specify:
1. Average price before discounts
2. Expected discounted price
3. Best time to buy (before/during/after Black Friday)
4. Alternatives
Format the results as a table.

And here’s a prompt for store comparison:

Product: [name and model]
Found in stores:
- [Store 1]: [price], shipping [terms]
- [Store 2]: [price], shipping [terms]
- [Store 3]: [price], shipping [terms]
Which option is more cost-effective considering the total cost? Analyze the reliability of the stores.

Claude is particularly good at analyzing large volumes of text and highlighting key points. Here’s a Claude prompt for analyzing reviews:

Here's a selection of reviews for [name] from various platforms: [insert reviews].
Analyze them and highlight:
1. Key advantages (top 3)
2. Key disadvantages (top 3)
3. Who is this product best suited for, and who should avoid it?
4. Are there any alarming issues mentioned?
5. Overall recommendation: is this worth buying?

Long-term planning prompt:

You're a financial consultant. I'm planning a major purchase: [product] for [price].
My monthly income: [amount]. My savings: [amount].
Should I buy this on Black Friday or should I wait?
What alternative saving and purchasing strategies can you offer?

Gemini offers seamless integration with the Google ecosystem and provides in-depth capabilities when working with images. Attach a screenshot of the banner or the offer on the website and write the prompt:

This is a Black Friday offer. Evaluate:
1. How attractive is this discount?
2. What information should I check additionally?
3. What should I pay attention to in the description?
4. Signs of a possible scam

Quick search prompt:

Find the best Black Friday 2025 offers in [category].
I'm looking for: [product characteristics]
Budget: [amount]
Region: [country/city]
Show the top-5 options and provide a justification for each choice.

Final checklist

1. Use AI to create a wish list, and start tracking prices with tools like CamelCamelCamel, Keepa, or other similar services. Set up convenient price-drop notifications.
2. Analyze the collected price data, find alternative products and stores, and simultaneously verify the sellers’ reliability.
3. Set up a separate credit card for purchases with a spending limit. If possible, get a virtual card and prepare our prompts for quick retail-offer analysis.
4. On the actual sale day, don’t fall for urgency tricks like “last item in stock!”, and make sure you check every “super deal” with your AI assistant and a critical eye. Cross-reference the price history, don’t open suspicious emails, and don’t follow dubious links. If you follow these steps, your Black Friday will result not only in zero losses, but also in genuinely advantageous purchases.

What else to read on the topic of AI:

• Privacy settings in ChatGPT
• DeepSeek: configuring privacy and deploying a local version
• The hidden dangers of AI coding
• New types of attacks on AI-powered assistants and chatbots
• How phishers and scammers use AI

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