Leveraging AI for enhanced data access control in distributed databases

It takes an average of 6,888 hours to detect a data breach and 8,808 hours to patch it up.

Those are some scary numbers. But that’s just the tip of the iceberg. In the meantime, the breach:

• Can cost a company over $4 million on average.
• Reduce revenue by a little over $1 million.

Not to mention, it causes a loss of reputation once customers find out; or worse, if your company’s big enough to catch media attention. 

With organizations continuing to collect large amounts of data and work remotely, data breaches show no signs of slowing down. Why? Internal negligence, weak security measures, or masterminded criminals are the typical causes.

The only way to minimize and eliminate data breaches is to: 

• Invest in a way to detect and stamp out anomalies.
• Make sure only the right people access the data.
• Secure local and cloud environments.

Thankfully, with artificial intelligence (AI), organizations of any size can take data access control to the next level. In this blog, we’ll explain how AI can enhance your current data management processes, specifically related to access control.

Machine learning

Traditionally, a database system operates on explicitly programmed instructions. In contrast, machine learning (ML) learns to make decisions from data. That’s part of the magic behind AI.

There are four main types of machine learning. All methods employ training algorithms to learn independently:

• Supervised learning: In this type of model, data scientists provide input, output, and feedback to build models. Some examples of supervised learning training algorithms include linear regression (sales forecasting, risk assessment), support vector machines (image classification, financial performance comparison), and decision trees (predictive analytics, pricing).
  
  
• Unsupervised learning: This model uses deep learning to arrive at conclusions and patterns through unlabeled training data. Some examples of unsupervised learning training algorithms include Apriori (sales functions, word associations), K-means clustering (performance monitoring, searcher intent), and artificial neural networks (data mining and pattern recognition).
  
  
• Semi-supervised learning: This type builds a model through a mix of labeled and unlabeled data, a set of categories, suggestions, and example labels. Some example training algorithms include generative adversarial networks (audio and video manipulation, data creation) and self-trained Naive Bayes classifiers (natural language processing).
  
  
• Reinforcement learning: This model is based on a system of rewards and punishments learned through trial and error, seeking maximum reward. Some examples of training algorithms include Q-learning (policy creation, consumption reductions) and model-based value estimation (linear tasks, estimating parameters).

For example, through deep learning, AI can separate malicious and genuine access attempts and help your organization’s IT team keep its data secure. More on this in a bit.

Predictive analytics

Through ML, AI systems perform predictive analytics. In other words, they analyze patterns, trends, and behaviors within user access attempts to forecast potential security threats. 

They can also inform preemptive measures to enhance database security. Here’s how it works in six simple steps.

Step 1: Historical data analysis

The more high-quality and real-time data AI has to work with, the better it’ll be at fortifying access to your data. It starts by collecting and aggregating previous user login data, including:

• The types of data every user accesses.
• The frequency of every user’s access.
• The access levels of different users.
• Login times.

With this information, the AI will learn more about how an authentic user behaves. The more data the model can analyze, the better. 

Additionally, consider leveraging machine learning in your recruitment processes as well. By analyzing historical data on successful hires, access frequency, and employee behaviors, AI can help identify patterns that lead to effective recruiting strategies.

Step 2: Pattern recognition

Once the AI has enough data, it can start noting down and recording the patterns that pertain to legitimate database access and behavior patterns. It’d do this for every variable possible, from user access level to the frequency of access. 

The process of data analysis and pattern recognition is continual because user behavior will likely change regularly depending on staff turnover and changes in business processes. 

Step 3: Anomaly detection

Anytime the AI system detects a pattern that holds a deviation, it’ll flag it as an anomaly. This could be a wide range of things, such as:

• A user is accessing the database at a time they’ve never accessed it before.
• A user makes more logins than usual during a 24-hour period.
• Multiple logins from the same user from different locations.
• Access from an unrecognized IP address or device.
• Uncharacteristic data modification patterns.
• Unusually high failed access attempts.
• Unusual download patterns.

Step 4: Risk assessment scoring

Not all anomalies are created equal, so each is given a unique risk score. This risk score is based on the potential impact the anomaly could have on the organization and the severity of the anomaly itself.

For example, a user logging in more times than usual isn’t necessarily an anomaly worth panicking over. Someone might just have needed the access on that day.

On the other hand, if Kacper Nowakowski from Wrocław, Poland, is suddenly accessing the database from Miami (and he’s not been given authority to work from Miami), that’s a serious threat that needs attention. 

Step 5: Threat identification and review

Each anomaly is brought to the attention of the database management system administrator for review. This could be the IT security team or a dedicated cybersecurity unit. Thankfully, the AI constantly monitors all access variables and will flag the slightest anomaly.

Image created with Dall-E

The notification appears on their dashboard along with the risk level. The threat is reviewed against a pre-determined governance policy to get a complete picture of what’s happening. An initial assessment may include:

1. Contacting the user in question to verify access.
2. Checking their account and access history.
3. Checking if there are any anomalies in data, that is, if any data has been edited, modified, or deleted.
4. Comparing it with other threats to detect a pattern.

If the team concludes the threat is real, further action is required. In that case, it’ll:

• Communicate the necessary protocols, such as business continuity plans.
• Change or disable user credentials for damage limitation.
• Perform an impact assessment.

Step 6: Post-incident analysis

AI isn’t perfect. It relies on high-quality data for inputs. Once your organization reflects on lessons learned from the incident, it can adjust the AI systems parameters as needed. For example:

• If it was a false alarm, the team might decide to lower or eliminate any risk score associated with this pattern in the future.
• If it was a particularly damaging incident, it can up the ante in the case of a repeat to ensure that action can be taken quicker.
• The team can feed it more accurate and up-to-date user profiles and access levels based on promotions, leavers, and new hires.

Additionally, the AI can make its own recommendations. For example, if it knows that Kacper will only access the database from Wrocław from Mondays to Fridays between 12 p.m. and 3 p.m., it can suggest that the team only keeps his account active at those times.

AI can also be used to help prevent data breach issues and improve identity and access management. With AI, teams can:

• Detect malicious conduct
• Gather data for user behavior analysis and monitoring
• Implement automatic remediation solutions
• Alert about potential threats
• Help enforce policies without the need for manual input

Sometimes, we need the help of AI. Why? Because humans make decisions based on emotions. 

For example, we’re more likely to turn the other way when an employee who’s been at the organization for 15 years has accessed sensitive data more frequently in the last six months. It’s unusual, but they’ve been around for over a decade, so they can be trusted, right?

Well, the AI model might tell you this is an insider threat. It’s not a guarantee that it is, but AI can make suggestions that a colleague might not and determine that this is something the team should investigate.

Integrating AI across multiple databases

Integrating AI systems with other AI components, like knowledge-based systems, offers a blend of dynamic learning and established expertise: a recipe for success. 

Let’s take a look at a healthcare database management scenario for reference. An ML algorithm can track and analyze access patterns of medical staff to patient records and learn normal access behaviors over time.

Meanwhile, a knowledge-based system equipped with regulatory and ethical guidelines on patient data confidentiality ensures that access controls align with compliance standards. When a doctor suddenly accesses an unusually high number of patient records, the ML system flags this as an anomaly.

The knowledge-based system then cross-references this behavior against compliance rules to determine if this access pattern might breach patient confidentiality. The system automatically restricts access and alerts the compliance team if a potential breach is detected. There’s a prediction that by the end of 2025, over 95% of customer interactions will be AI-driven, which creates an emerging trend for this kind of technology.

This synergy between ML’s adaptive learning and the rule-based logic of knowledge systems provides a more layered, intelligent approach to database security. It’s capable of adapting to new challenges while ensuring that you are meeting all compliance requirements.

Using AI to automate access control for data analytics platforms

Some data analytics platforms use AI to automate and optimize data access control. For example, some platforms use natural language processing to understand user queries and provide relevant data without exposing sensitive information. 

Others use ML to learn user behavior and preferences and suggest personalized insights and actions. Using AI, data analytics platforms can enhance data security, governance, and usability for distributed databases. 

Joe Troyer, the founder behind Review Grower, home to a potent local citation finder, emphasizes that “by using AI-driven data access control, businesses can not only ensure the security and governance of their distributed databases but also empower users with personalized insights, thereby creating a user-friendly data analytics environment”.

Advantages of AI data access controls

The benefits of proper data management through AI are clear. Let’s take a look at them in more detail. 

Improved security 

AI quickly and proactively identifies and prevents potential breaches. Prevention is any organization’s highest priority, and AI is a giant step in the right direction for making better business decisions. It’s easier than ever to prevent unauthorized access. 

Environmental benefits

Anthony Vicinelly, former Federal Technology Director at Nlyte Software, states that since 1998, the number of federal data centers has skyrocketed from roughly 400 to over 10,000. There’s no real consideration of how we can better manage and use them. 

AI algorithms can analyze real-time data on energy usage, cooling systems, and overall infrastructure to identify areas of inefficiencies. That way, we can do better for our planet as we rely more and more on technology. 

Improvements to the digital customer journey

The integration of AI in data access control transforms the digital customer journey, ensuring a secure and personalized experience. By analyzing customer data and behavior, AI enables dynamic segmentation and content delivery, enhancing data privacy and trust. 

This approach helps you comply with regulations, but it also enriches the customer journey, which in turn boosts loyalty and satisfaction.

Challenges of AI data access controls

Before we get whisked away by the exciting possibilities of AI, it’s wise to know its challenges. Let’s peel back the curtains and consider some potential drawbacks before you go all in on adding AI to your workflows.

Data privacy concerns

AI systems require access to large amounts of data to make accurate predictions. However, organizations must ensure that privacy regulations are followed and that sensitive data is adequately protected.

False positives and false negatives

AI algorithms may produce false positives where authorized users are denied access or false negatives where unauthorized users gain access. Continuous monitoring and fine-tuning of AI models can help mitigate these risks. AI is only as good as the humans who manage it.

Bolster your data access control with artificial intelligence

And there you have it: everything you need to know about enhancing data access control in your organization. The good, the bad, and the ugly.

Artificial intelligence is here to stay. It’s up to you how you choose to integrate it into your data management solutions. 

The investment in a data management platform powered by AI is worth taking. It helps build a wall of protection for sensitive company information and safeguards your assets. With that, we’ll leave you with the key takeaways for today to help you improve your data management plan. 

1. Data breaches are costly in terms of money and reputation.
   
   
2. The integration of AI should be part of the modern data management system, and is a significant upgrade in data security and the management process because:
   • Business intelligence tools use machine learning to understand independently what’s deemed genuine or malicious access.
   • Data management tools proactively flag threats and score them for the organization to investigate.
   • Data management software learns from each incident to become even better.
     
     
3. While AI has advantages, there are challenges of false positives and negatives and ensuring it’s given enough non-biased data to work with.