Top 5 Email Verification and Validation APIs for your Product

If you have spent some time running a website or online business, you would be aware of the importance of emails.

What many see as a decadent communication medium still holds immense value for digital marketers.

More than 330 billion emails are sent every day, even in 2022.

While email marketing is very effective, it is very difficult to do it right. One of the key reasons being the many problems that email marketers face with their email lists. Are the email IDs correct? Do they have spam traps? Are these disposable email addresses? There are a multitude of questions to deal with in email marketing and newsletter campaigns.

Email verification and validation APIs help us deal with this problem. APIs integrate with your platform and automatically check all email addresses for spam, mistyping, fake email ids, and so on. 

Top 5 email verification and validation APIs for your product

Today we will talk about the 5 best APIs that you can use to validate and verify the email addresses in your mailing list. Using an API can be a gamechanger for many email marketers. Before we get into the top 5 list, let’s discuss why APIs are so effective and how they work. 

Why APIs are so efficient

The major reason APIs work so efficiently is that it does not require human supervision. APIs work automatically and users do not have to manually configure them each time. The ease of use is one among many reasons you should start using an email verification and validation API.

If you maintain a mailing list, you would also want to know where your effort is going. All email marketers spend considerable time perfecting their emails. On top of that, they need to use an email marketing platform like Klaviyo. An API ensures that your hard work does not go in vain. By filtering out fake and disposable email IDs, you get a better idea of where your mailing stand stands. As a result, when you use a platform like Klaviyo along with an email verification API, the results are much better. In case you want something other than Klaviyo, you can learn more about Klaviyo alternatives here. 

How email verification and validation APIs work

Email verification and validation APIs work primarily in 7 ways:

  • Syntax Check
  • Address Name Detection
  • DEA (disposable email address) Detection
  • Spam Trap Detection
  • DNSBL and URI DNSBL Check
  • MX Record Lookup
  • Active Mailbox Check

With the help of these email verification and validation methods, you will see much better results from your email marketing campaign. On top of that, your business will not be identified as spam and will help in building reputation and authority.

Now that we have some idea about what email verification APIs are and what they do, let’s head over to the list. 

1. Abstract API

Abstract API is one of the most popular email verification and validation APIs out there. Here are some of its key features:

  • MX record check
  • GDPR and CCPA compliant
  • Does not store any email
  • Role email check

If you have looked for email address validation API on the internet, you must have come across Abstract API. It is among the best in the business and also comes with affordable subscription plans.

Abstract API helps with bounce rate detection, spam signups, differentiating between personal and business email IDs, and a lot more. However, the most significant feature of Abstract API is that it allows up to 500 free email checks every month. That’s a great way to see whether the product works for you before subscribing to it.

Abstract API is user-friendly and budget-friendly, which makes it a top choice for many email marketers. Anyone new to using these tools can easily learn about them from Abstract API. For these reasons, Abstract API has the number one spot on our list. 

2. SendGrid Validation API

After Abstract API, the second product to have top-notch features is SendGrid Validation API.  Here are its key features:

  • Uses machine learning to verify email addresses in real-time
  • Accurately identifies all inactive or inaccurate email addresses
  • You can check how your email appears in different mailboxes
  • Gives risk scores for all email addresses

While most email verification and validation APIs work similarly, SendGrid Validation API takes it a notch higher with machine learning and artificial intelligence. Despite having advanced features and functionalities, SendGrid Validation API is not difficult to use.

SendGrid Validation API operates on the cloud and does not store any of your email addresses. OIn top of that, there are easy settings and configuration options that users can tweak with. However, SendGrid Validation API does not have any free offering. There are only two plans: pro and premier. Users have to pay $89.95 per month to access SendGrid Validation API.

If you are looking for advanced email verification and validation API, no need to look beyond SendGrid Validation API. It has everything you would need for a solid email marketing campaign apart from having many additional features. 

3. Captain Verify

Another email verification and validation API – Captain Verify – is a one-stop solution for all email verification needs. Here are its key features:

  • Get reports on the overall quality of your email address database
  • Affordable plans
  • Compliant with GDPR regulations
  • Export encrypted CSV files

Unlike other email verification and validation APIs, Captain Verify does not stop after verifying the emails for spam, fake or invalid addresses, and so on. It helps email marketers understand how their campaign is performing and gives detailed reports on returns on investment. It is one of the best APIs available for the overall growth of your mailing campaign.

If you are looking for something simple yet powerful, Captain Verify will be a great option. Along with the features we mentioned already, it also lets users filter and refine their email lists. It can help you understand the overall quality of your mailing list much better.

As you can see, Captain Verify ticks most of the boxes to be one of the best email verification and validation APIs out there. Anyone looking for a good email API should give it a go. The best thing is that users get all this and more at only $7 per 1000 emails. 

4. Mailgun

Mailgun earns the fourth spot on our list. However, that does not mean it is any way less than the previous options discussed. Here’s what it offers:

  • RFC standards compliant
  • Daily and hourly tracking of API usage
  • Has a bulk list validation tools for faster operations
  • Supports both CSV and JSON format
  • Track bounce and unsubscribe rates

Email marketers around the world prefer Mailgun for all their email verification and validation needs. It has multiple features that allow users to check their mailing list for fakes and scams. Apart from that, it also gives users a good idea of how their marketing campaign is performing.

Mailgun enjoys high ratings across review platforms like Capterra and G2. People use it for a wide range of purposes, but email verification and validation remain the most important. Mailgun keeps track of bounce rates, hard bounce rates, and unsubscribe rates. With the help of these stats, email marketers can measure how their campaign is doing.

If you are looking for a simple email verification and validation tool, Mailgun can be a good choice. It is worth trying for anyone who wants to take their email marketing to the next level.

5. Hunter

Our last entry to the list is Hunter. It is a well-known API that is widely used by email marketers. Here’s what it gets right:

  • Compare your mailing list with the Hunter mailing list for comparative quality analysis
  • SMTP checks, domain information verification, and multi-layer validation
  • Easy integration with Google Sheets
  • Supports both CSV and .txt formats

Hunter gives what it calls confidence scores which represent how strong or weak your mailing list is. This email verification and validation tool follows all the checks that we mentioned earlier, including SMTP verification, gibberish detection, MX record checks, and more. These features have worked together to make Hunter one of the most popular email verification and validation tools.

Hunter email verification API integrates easily with any platform and has a user-friendly interface. It also has a free plan that lets users check up to 50 emails for free. Giving it a try without spending money is very useful for anyone looking for a new email verification and validation API.

If you are looking for an email finder and email verifier rolled into one, Hunter is the best solution. With so many features and functionalities, it is one of the favorite email verification and validation APIs for thousands of marketers and entrepreneurs.


When used correctly, email verification and validation APIs can give any online business a significant boost. As an email marketer, digital marketer, website owner, or entrepreneur, you should be using one of these APIs. If you aren’t using one already, find your top pick from our list of the 5 best email verification and validation APIs.

Variational Autoencoders

After Deep Autoregressive Models and Deep Generative Modelling, we will continue our discussion with Variational AutoEncoders (VAEs) after covering up DGM basics and AGMs. Variational autoencoders (VAEs) are a deep learning method to produce synthetic data (images, texts) by learning the latent representations of the training data. AGMs are sequential models and generate data based on previous data points by defining tractable conditionals. On the other hand, VAEs are using latent variable models to infer hidden structure in the underlying data by using the following intractable distribution function: 

(1)   \begin{equation*} p_\theta(x) = \int p_\theta(x|z)p_\theta(z) dz. \end{equation*}

The generative process using the above equation can be expressed in the form of a directed graph as shown in Figure ?? (the decoder part), where latent variable z\sim p_\theta(z) produces meaningful information of x \sim p_\theta(x|z).

Architectures AE and VAE based on the bottleneck architecture. The decoder part work as a generative model during inference.

Figure 1: Architectures AE and VAE based on the bottleneck architecture. The decoder part work as
a generative model during inference.


Autoencoders (AEs) are the key part of VAEs and are an unsupervised representation learning technique and consist of two main parts, the encoder and the decoder (see Figure ??). The encoders are deep neural networks (mostly convolutional neural networks with imaging data) to learn a lower-dimensional feature representation from training data. The learned latent feature representation z usually has a much lower dimension than input x and has the most dominant features of x. The encoders are learning features by performing the convolution at different levels and compression is happening via max-pooling.

On the other hand, the decoders, which are also a deep convolutional neural network are reversing the encoder’s operation. They try to reconstruct the original data x from the latent representation z using the up-sampling convolutions. The decoders are pretty similar to VAEs generative models as shown in Figure 1, where synthetic images will be generated using the latent variable z.

During the training of autoencoders, we would like to utilize the unlabeled data and try to minimize the following quadratic loss function:

(2)   \begin{equation*} \mathcal{L}(\theta, \phi) = ||x-\hat{x}||^2, \end{equation*}

The above equation tries to minimize the distance between the original input and reconstructed image as shown in Figure 1.

Variational autoencoders

VAEs are motivated by the decoder part of AEs which can generate the data from latent representation and they are a probabilistic version of AEs which allows us to generate synthetic data with different attributes. VAE can be seen as the decoder part of AE, which learns the set parameters \theta to approximate the conditional p_\theta(x|z) to generate images based on a sample from a true prior, z\sim p_\theta(z). The true prior p_\theta(z) are generally of Gaussian distribution.

Network Architecture

VAE has a quite similar architecture to AE except for the bottleneck part as shown in Figure 2. in AES, the encoder converts high dimensional input data to low dimensional latent representation in a vector form. On the other hand, VAE’s encoder learns the mean vector and standard deviation diagonal matrix such that z\sim \matcal{N}(\mu_z, \Sigma_x) as it will be performing probabilistic generation of data. Therefore the encoder and decoder should be probabilistic.


Similar to AGMs training, we would like to maximize the likelihood of the training data. The likelihood of the data for VAEs are mentioned in Equation 1 and the first term p_\theta(x|z) will be approximated by neural network and the second term p(x) prior distribution, which is a Gaussian function, therefore, both of them are tractable. However, the integration won’t be tractable because of the high dimensionality of data.

To solve this problem of intractability, the encoder part of AE was utilized to learn the set of parameters \phi to approximate the conditional q_\phi (z|x). Furthermore, the conditional q_\phi (z|x) will approximate the posterior p_\theta (z|x), which is intractable. This additional encoder part will help to derive a lower bound on the data likelihood that will make the likelihood function tractable. In the following we will derive the lower bound of the likelihood function:

(3)   \begin{equation*} \begin{flalign} \begin{aligned} log \: p_\theta (x) = & \mathbf{E}_{z\sim q_\phi(z|x)} \Bigg[log \: \frac{p_\theta (x|z) p_\theta (z)}{p_\theta (z|x)} \: \frac{q_\phi(z|x)}{q_\phi(z|x)}\Bigg] \\ = & \mathbf{E}_{z\sim q_\phi(z|x)} \Bigg[log \: p_\theta (x|z)\Bigg] - \mathbf{E}_{z\sim q_\phi(z|x)} \Bigg[log \: \frac{q_\phi (z|x)} {p_\theta (z)}\Bigg] + \mathbf{E}_{z\sim q_\phi(z|x)} \Bigg[log \: \frac{q_\phi (z|x)}{p_\theta (z|x)}\Bigg] \\ = & \mathbf{E}_{z\sim q_\phi(z|x)} \Big[log \: p_\theta (x|z)\Big] - \mathbf{D}_{KL}(q_\phi (z|x), p_\theta (z)) + \mathbf{D}_{KL}(q_\phi (z|x), p_\theta (z|x)). \end{aligned} \end{flalign} \end{equation*}

In the above equation, the first line computes the likelihood using the logarithmic of p_\theta (x) and then it is expanded using Bayes theorem with additional constant q_\phi(z|x) multiplication. In the next line, it is expanded using the logarithmic rule and then rearranged. Furthermore, the last two terms in the second line are the definition of KL divergence and the third line is expressed in the same.

In the last line, the first term is representing the reconstruction loss and it will be approximated by the decoder network. This term can be estimated by the reparametrization trick \cite{}. The second term is KL divergence between prior distribution p_\theta(z) and the encoder function q_\phi (z|x), both of these functions are following the Gaussian distribution and has the closed-form solution and are tractable. The last term is intractable due to p_\theta (z|x). However, KL divergence computes the distance between two probability densities and it is always positive. By using this property, the above equation can be approximated as:

(4)   \begin{equation*} log \: p_\theta (x)\geq \mathcal{L}(x, \phi, \theta) , \: \text{where} \: \mathcal{L}(x, \phi, \theta) = \mathbf{E}_{z\sim q_\phi(z|x)} \Big[log \: p_\theta (x|z)\Big] - \mathbf{D}_{KL}(q_\phi (z|x), p_\theta (z)). \end{equation*}

In the above equation, the term \mathcal{L}(x, \phi, \theta) is presenting the tractable lower bound for the optimization and is also termed as ELBO (Evidence Lower Bound Optimization). During the training process, we maximize ELBO using the following equation:

(5)   \begin{equation*} \operatorname*{argmax}_{\phi, \theta} \sum_{x\in X} \mathcal{L}(x, \phi, \theta). \end{equation*}


Furthermore, the reconstruction loss term can be written using Equation 2 as the decoder output is assumed to be following Gaussian distribution. Therefore, this term can be easily transformed to mean squared error (MSE).

During the implementation, the architecture part is straightforward and can be found here. The user has to define the size of latent space, which will be vital in the reconstruction process. Furthermore, the loss function can be minimized using ADAM optimizer with a fixed batch size and a fixed number of epochs.

Figure 2: The results obtained from vanilla VAE (left) and a recent VAE-based generative model NVAE (right)

Figure 2: The results obtained from vanilla VAE (left) and a recent VAE-based generative
model NVAE (right)

In the above, we are showing the quality improvement since VAE was introduced by Kingma and
Welling [KW14]. NVAE is a relatively new method using a deep hierarchical VAE [VK21].


In this blog, we discussed variational autoencoders along with the basics of autoencoders. We covered
the main difference between AEs and VAEs along with the derivation of lower bound in VAEs. We
have shown using two different VAE based methods that VAE is still active research because in general,
it produces a blurry outcome.

Further readings

Here are the couple of links to learn further about VAE-related concepts:
1. To learn basics of probability concepts, which were used in this blog, you can check this article.
2. To learn more recent and effective VAE-based methods, check out NVAE.
3. To understand and utilize a more advance loss function, please refer to this article.


[KW14] Diederik P Kingma and Max Welling. Auto-encoding variational bayes, 2014.
[VK21] Arash Vahdat and Jan Kautz. Nvae: A deep hierarchical variational autoencoder, 2021.

How to choose the best pre-trained model for your Convolutional Neural Network?

Introduction to Transfer Learning 

Let’s start by defining this term that is increasingly used in Data Science:

Transfer Learning refers to the set of methods that allow the transfer of knowledge acquired from solving a given problem to another problem.

Transfer Learning has been very successful with the rise of Deep Learning.  Indeed, the models used in this field often require high computation times and important resources. However, by using pre-trained models as a starting point, Transfer Learning makes it possible to quickly develop high-performance models and efficiently solve complex problems in Computer Vision.

Usual Machine Learning Approach vs Transfer Learning

As most of the Deep learning technics, Transfer Learning is strongly inspired by the process with which we learn.

Let’s take the example of someone who masters the guitar and wants to learn to play the piano. He can capitalize on his knowledge of music to learn to play a new instrument. In the same way, a car recognition model can be quickly adapted to truck recognition.

How is Transfer Learning concretely implemented to solve Computer Vision problems?

Now that we have defined Transfer Learning, let’s look at its application to Deep Learning problems, a field in which it is currently enjoying great success.

The use of Transfer Learning methods in Deep Learning consists mainly in exploiting pre-trained neural networks

Generally, these models correspond to very powerful algorithms that have been developed and trained on large databases and are now freely shared.

In this context, 2 types of strategies can be distinguished:

  1. Use of pre-trained models as feature extractors:

The architecture of Deep Learning models is very often presented as a stack of layers of neurons. These layers learn different features depending on the level at which they are located. The last layer (usually a fully connected layer, in the case of supervised learning) is used to obtain the final output. The figure below illustrates the architecture of a Deep Learning model used for cat/dog detection. The deeper the layer, the more specific features can be extracted.


Architecture of CNN

The idea is to reuse a pre-trained network without its final layer. This new network then works as a fixed feature extractor for other tasks.

To illustrate this strategy, let’s take the case where we want to create a model able to identify the species of a flower from its image. It is then possible to use the first layers of the convolutional neural network model AlexNet, initially trained on the ImageNet image database for image classification.

  1. Fitting of pre-trained models:

This is a more complex technique, in which not only the last layer is replaced to perform classification or regression, but other layers are also selectively re-trained. Indeed, deep neural networks are highly configurable architectures with various hyperparameters. Moreover, while the first layers capture generic features, the last layers focus more on the specific task at hand.

So the idea is to freeze (i.e. fix the weights) of some layers during training and refine the rest to meet the problem. 

This strategy allows to reuse the knowledge in terms of the global architecture of the network and to exploit its states as a starting point for training. It thus allows to obtain better performances with a shorter training time.

The figure below summarizes the main Transfer Learning approaches commonly used in Deep Learning.


Re-use of pre-trained machine learning models in transfer learning

How to choose your pre-trained CNN ?

TensorFlow and Pytorch have built very accessible libraries of pre-trained models easily integrable to your pipelines, allowing the simple leveraging of the Transfer learning power.
In the first part you discovered what a pre-trained model is, let’s now dig into how to choose between the (very) large catalog of models accessible in open-source.

An unresolved question:

As you could have expected, there is no simple answer to this question. Actually, many developers just stick to the models they are used to and that performed well in their previous projects.
However, it is still possible to follow a few guidelines that can help you decide.


The two main aspects to take into account are the same as most of the machine learning tasks :
⦁ Accuracy : The Higher, the better
⦁ Speed : The Faster, the better

The dream being having a model that has a super fast training with an excellent accuracy. But as you could expect, usually to have a better accuracy, a deeper model is needed, therefore a model that takes more time to train. Thus, the goal is to maximize the tradeoff between accuracy and complexity. You can observe this tradeoff in the following graph taken from the Efficient Net model original paper.

Accuracy on Imagenet

As you can observe on this graph, bigger models are not always better. There is always a risk that a more complex model overfits your data, because it can give too much importance to subtle details in features. Knowing that the best is to start with the smallest model, that is what’s done in the industry. A “good-enough” model that is small and therefore quickly trained is preferred. Of course if you aim for great accuracy with no interest in a quick training then you can target the large model and even try ensemble techniques combining multiple models power.

Most performant models at this time :

Here are a few models that are widely used today in the field of computer vision. From image classification to complex image captioning, those structures offers great performances :

  • ResNet50
  • EfficientNet
  • Inceptionv3

ResNet 50 : ResNet was developed by Microsoft and aims at resolving the ‘vanishing gradient problem’. It allows the creation of a very deep model (up to a hundred layers).

Top-1 accuracy : 74.9%

Top-5 accuracy : 92.1%

Size : 98MB

Parameters : 26 millions

EfficientNet : This model is a state-of-the art convolutional neural network trained by Google. It is based on the same construction as ResNet but with an intelligent rescaling method.

Top-1 accuracy : 77.1%

Top-5 accuracy : 93.3.0%

Size : 29MB

Parameters : 5 millions

InceptionV3 : Inception Networks (GoogLeNet/Inception v1) have proved to be more computationally efficient, both in terms of the number of parameters generated by the network and the economical cost incurred. It is based on Factorized Convolutions.

Top-1 accuracy : 77.9%

Top-5 accuracy : 93.7%

Size : 92MB

Parameters : 24 millions

Final Note: 

To summarize, in this article, we have seen that Transfer Learning is the ability to use existing knowledge, developed to solve a given problem, to solve a new problem. We saw the top 3 State-of-the-Art pre-trained models for image classification. Here I summarized the performance and some detail on each of those models.

tabel of pre-trained ai models

However, as you have now understood, this is a continuously growing domain and there is always a new model to look forward to and push the boundaries further. The best way to keep up is to read papers introducing new model construction and try the most performing new releases.


Key Points on AI’s Role In The Future Of Data Protection

Artificial Intelligence is transforming every industry as we speak, and data protection might be the biggest of them all. With a projected market size of USD 113390 Million, there’s a lot to protect—and humans won’t be able to do it all.

Luckily for us, Artificial Intelligence solutions are here to help us out. Because AI can do a lot more than just collect and analyze data — it can also protect it. In this article, we’ll explain what the role of Artificial Intelligence is in the future of data protection.

Here’s AI for data protection in summary:

3 Ways AI serves in data protection

  • AI Can Improve Compliance: from the GDPR to the CPRA, AI can help you track down gaps in your compliance with the most important data protection legislation.
  • AI as an ally against cyberattacks: cyberattacks are becoming increasingly sophisticated, but so is AI. It can help you recognize the patterns that indicate an attack is underway and put in automated reactions to minimize damage.
  • AI can protect against phishing attempts: together with ML and NLP, AI is a valuable tool in detecting phishing attempts—especially since they are becoming increasingly hard to spot.

Why AI is so valuable in the fight against cybercrime

  • AI can handle more and more complex data than humans: with the amount of data that is being processed and collected every second, it’s incredibly inefficient to not let AI do the work—and AI can cut costs drastically as well.
  • AI can quickly classify data and keep it organized: before you can protect your data, make sure it’s organized properly. No matter the amount or complexity of the structure, AI can help you stay on top of it.
  • No humans needed to keep sensitive data secure: scared of human errors and have trust issues? With AI, you don’t need to rely on people for protection and discreteness.

The threats your data faces on a daily basis

It’s not just the good guys who are using technologies like artificial intelligence to up their game—hackers and people after sensitive data can also reap the benefits of AI. There are more than 2,200 cyberattacks per day—which means one every 39 seconds, so the threat is substantial.

While the clock is ticking, research found that fewer than 25% of businesses think they’re ready to fight off a ransomware attack. That leaves 75% of organizations all the more vulnerable to data privacy threats.

Leaks of personal information, data hacks and other privacy scandals are costly: it’s estimated that cybercrime will cost companies worldwide an estimated $10.5 trillion annually by 2025, with an ​​average cost of $3.86 million per breach—not including the harm done to users and the reputation of a business.

That makes investing in a solid data protection system all the more useful, which is shown in the spending habits of businesses all over the world: global spending on privacy efforts are expected to reach $8 billion by 2022. Luckily, with the rapid developments in AI and other smart security tools, it has become more attainable—even for smaller businesses.

3 Ways AI serves in data protection

What does Artificial intelligence in data protection look like in practice? Let’s look at some of the ways AI can assist your organization in warding off cyber criminals.

1.    AI Can Improve Compliance

How compliant is your organization with all the data protection and privacy regulations? It can be incredibly hard to keep up, understand and check whether your systems are up-to-date on the latest compliance regulations.

But—no need to worry! AI has taken over the nitty-gritty of it all. It’s expected that by 2023, over 40% of privacy compliance technology will rely on AI.

What kind of legislation can you hold up with the use of AI? Two big names are the GDPR and CPRA. AI can help you identify blind spots in your data protection efforts and warn you when you’re not living up to the standards governments put in place.

One tool that does this is With AI-driven PI data discovery, DSR automation, documented accountability you get a clearer view of your data processing activities and can make sure you’re compliant.

An alternative AI solution is Claudette, a web crawler that assesses the privacy policies using supervised machine learning technologies. After it’s done scanning and collecting information, it checks if the data is used in a way that’s GDPR proof. It shows you issues such as incomplete information, unclear language, or problematic data processing tactics.

Of course, you can’t solely rely on AI to do all the work when it comes to privacy and data protection. You and your employees also need to understand and handle data in ways that are compliant with the rules set in place.

Start with understanding what the GDPR and CPRA are all about. Osano’s guide to CPRA is a great place to start to learn what the CPRA, which will replace the CPPA on January 1, 2023, is all about. Educate yourself on the rules of data protection, and it will be even easier to select an AI tool that will help you protect your valuable data.

2.    AI as an ally against cyberattacks

With the combination of big data, artificial intelligence and machine learning, you have a great recipe for tracking down the patterns that indicate a cyberattack is happening. Why is that helpful?

It’s all about identifying patterns. When AI and ML work together, they can map out what happened during previous attacks. Together, they can identify the actions hackers have taken before and find weak spots in your security system, so you can fill those gaps and be extra alert.

AI can assist in quickly alerting the right people and systems that there’s a threat. This can even kick off a series of extra measures to be taken, so the cyberattack can be beaten back.

AI can also make sure malicious websites and unauthorized data transactions are automatically blocked before any harm can be done.

3.    AI can protect against phishing attempts

​​Sometimes its employees who unknowingly are letting the cyber criminals in. Many people roll their eyes when they hear about yet another phishing attempt—shouldn’t we all know better by now not to click on certain links? — but cyber criminals are creating increasingly sophisticated phishing attacks. Even the most tech-savvy and internet-native people are able to fall for it.

Because phishing is all about what’s happening in the details, or in the background of a message—something the untrained human eye won’t immediately see.

Ai does see it, however. With technologies like Natural Language Processing and Machine Learning, it can automatically spot if a phishing attack is at play, and warn users.

There are even AI and ML tools on the market that are able to analyze the context of a message and the relationship between the sender and receiver, for even greater accuracy.

Why AI is so valuable in the fight against cybercrime

But why AI? Can we really rely on yet another robotic system to keep a digital framework safe? Isn’t it safe to have it handled by humans? We’ll expand on the three main benefits AI offers in the data protection game.

1.    AI can handle more and more complex data than humans

With all the data that is being processed and stored nowadays, there are barely enough people on the planet to keep an eye on every sensitive piece of information.

Good data protection is extremely time-consuming, because it’s constant. Checking servers manually is virtually impossible.

AI can work automatically and 24/7, no matter how much data there is to handle. On top of that, AI can be put in place to handle the more complex data structures, which can be hard to analyze and protect for humans. All while keeping costs low.

2.    AI can quickly classify data and keep it organized

Before you can even start protecting data, you will need to put it in place—efficiently. With the large volumes of data that organizations deal with, AI comes in handy. AI can quickly classify and manage data to keep it organized.

3.    No humans needed to keep sensitive data secure

AI can work independently from humans, which means nobody necessarily needs to have direct access to the sensitive data you’re trying to predict. Not only does that decrease the changes of human error, but it also builds an extra layer of trust.

Ready to call in the help of AI for your data protection?

Start by looking at the legislations that are important for your organization, and build on the needs you have for your specific business. Want to know more about the power of AI for data driven businesses? Keep reading in our blog section dedicated to artificial intelligence!