MARKETING
What You Need to Know About Dynamic Application Security Testing
One of the most essential approaches for protecting apps is Dynamic Application Security Testing or DAST. By identifying and mitigating vulnerabilities in real-time, DAST can help organizations prevent data breaches and other harmful attacks. In this article, we will explore what DAST is, how it works, and the top tools in the industry. We’ll go through the advantages and disadvantages of employing DAST so that you can make an educated decision about whether it’s appropriate for your company.
Understanding Dynamic Application Security Testing
DAST is a form of testing that examines an application’s security while it is executing. DAST can be used to identify flaws, such as cross-site scripting (XSS), SQL injection, and session hijacking.
What Makes DAST So Special?
The importance of DAST lies in its ability to find vulnerabilities in applications that are already deployed and in use. This is in contrast to static application security testing (SAST), which analyzes code without running the application, and thus can only find vulnerabilities that are present in the code itself.
List of Top DAST Tools
There are many different DAST tools on the market, each with its own strengths and weaknesses. Here are three of the top DAST tools:
- Astra’s Pentest
- AppScan
- Burp Suite
DAST Types & How Does It Work?
Black-box and white-box testing are two different types of DAST. Black-box testing assesses an application’s security without any knowledge of its internals, while white-box testing assesses an application’s security with full knowledge of its internals.
DAST works by scanning an application for vulnerabilities while it is running. This can be done either externally, from outside the network, or internally, from within the network. Internal scanning is typically used to analyze the security of web applications, while external scanning may be utilized to evaluate the security of both online and non-online programs.
Why Do You Need DAST for Your Application?
DAST is important for your application because it can find vulnerabilities that are not detectable by other methods. This is due to the fact that DAST scans applications in their running state, which allows it to find vulnerabilities that are not present in the code itself. Additionally, DAST has a number of other advantages, including:
- Very few false positives: Because DAST does not rely on syntactic knowledge of an application, it produces very few false positives (incorrectly identified vulnerabilities).
- No syntactic knowledge of application: Because DAST does not require syntactic knowledge of an application, it can be used to assess the security of both web and non-web applications.
- Real-world scenarios: DAST can simulate real-world attack scenarios, such as SQL injection and cross-site scripting (XSS), which makes it more effective at finding vulnerabilities.
- Scan what matters: DAST can be configured to scan only the parts of an application that are most likely to be vulnerable, which reduces the time and resources required for testing.
- Easy and continuous setup: DAST can be set up quickly and easily, and it can be run continuously so that new vulnerabilities can be found as they are introduced.
- Integration with SDLC: DAST may be used throughout the software development lifecycle (SDLC), allowing firms to discover and repair vulnerabilities early in the development cycle.
Top DAST Tools Further Explained
Now that we’ve looked at what DAST is and why it’s important, let’s take a more detailed look at some of the top DAST tools on the market.
Astra’s Vulnerability Scanner
The Astra Vulnerability Scanner is an on-demand security scanner that anyone may use to identify flaws in their software. It’s a cloud-based program that runs on any platform and requires an internet connection to access.
The scanner includes 3000+ scan rules, which are the natural hacker intellect discovered through vulnerability inspections and penetration tests (VAPT) performed by our security experts on numerous applications. Thorough knowledge of hacking methods utilized in security vulnerability scanning and penetration testing is required to identify original hacker intelligence.
AppScan
AppScan is a white-box testing tool that assesses the security of web and non-web applications. It works by scanning an application for vulnerabilities while it is running. AppScan can be used to identify vulnerabilities such as cross-site scripting (XSS), SQL injection, and session hijacking.
Burp Suite
Burp Suite is a black-box testing tool that assesses the security of web applications. It works by scanning an application for vulnerabilities while it is running. Burp Suite may be used to find and exploit a cross-site scripting (XSS), SQL injection, and session hijacking vulnerabilities.
Pros of DAST
DAST has a number of advantages over other methods of testing, including:
- DAST can find vulnerabilities that are not detectable by other methods
- DAST has very few false positives
- DAST does not require syntactic knowledge of an application
- DAST may be used to analyze the security of both web and non-web applications
- DAST can create realistic attack situations, such as SQL injection and cross-site scripting (XSS), to test your security measures
Cons of DAST
DAST also has some disadvantages, including:
- DAST is only effective against vulnerabilities that exist in the running state of an application
- DAST may take a long time and be costly in terms of resources
- DAST may cause an application to malfunction
- To be utilized effectively, DAST necessitates a high level of understanding and experience
Conclusion
DAST is a powerful tool that can be used to assess the security of web and non-web applications. It has a number of advantages, including the ability to find vulnerabilities that are not detectable by other methods, very few false positives, and no requirement for syntactic knowledge of an application. However, DAST also has some disadvantages, including the fact that it is only effective against vulnerabilities that exist in the running state of an application and it can be slow and resource-intensive. Overall, DAST is a valuable tool that should be considered when assessing the security of applications.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
Introduction
With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.
Types of YouTube Ads
Video Ads
- Description: These play before, during, or after a YouTube video on computers or mobile devices.
- Types:
- In-stream ads: Can be skippable or non-skippable.
- Bumper ads: Non-skippable, short ads that play before, during, or after a video.
Display Ads
- Description: These appear in different spots on YouTube and usually use text or static images.
- Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).
Companion Banners
- Description: Appears to the right of the YouTube player on desktop.
- Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.
In-feed Ads
- Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.
Outstream Ads
- Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.
Masthead Ads
- Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.
YouTube Ad Specs by Type
Skippable In-stream Video Ads
- Placement: Before, during, or after a YouTube video.
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Vertical: 9:16
- Square: 1:1
- Length:
- Awareness: 15-20 seconds
- Consideration: 2-3 minutes
- Action: 15-20 seconds
Non-skippable In-stream Video Ads
- Description: Must be watched completely before the main video.
- Length: 15 seconds (or 20 seconds in certain markets).
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Vertical: 9:16
- Square: 1:1
Bumper Ads
- Length: Maximum 6 seconds.
- File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
- Resolution:
- Horizontal: 640 x 360px
- Vertical: 480 x 360px
In-feed Ads
- Description: Show alongside YouTube content, like search results or the Home feed.
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Square: 1:1
- Length:
- Awareness: 15-20 seconds
- Consideration: 2-3 minutes
- Headline/Description:
- Headline: Up to 2 lines, 40 characters per line
- Description: Up to 2 lines, 35 characters per line
Display Ads
- Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
- Image Size: 300×60 pixels.
- File Type: GIF, JPG, PNG.
- File Size: Max 150KB.
- Max Animation Length: 30 seconds.
Outstream Ads
- Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
- Logo Specs:
- Square: 1:1 (200 x 200px).
- File Type: JPG, GIF, PNG.
- Max Size: 200KB.
Masthead Ads
- Description: High-visibility ads at the top of the YouTube homepage.
- Resolution: 1920 x 1080 or higher.
- File Type: JPG or PNG (without transparency).
Conclusion
YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
A deeper dive into data, personalization and Copilots
Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.
To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.
Dig deeper: Salesforce piles on the Einstein Copilots
Salesforce’s evolving architecture
It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?
“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”
Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”
That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.
“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.
Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”
Let’s learn more about Einstein Copilot
“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.
For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”
Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”
It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”
What’s new about Einstein Personalization
Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?
“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”
Finally, trust
One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.
“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”
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