5 AI Best Practices for Data Security

AI Cybersecurity Best Practices

After the COVID-19 outbreak, almost all global businesses have changed their way of working and communicating. The process of shifting whole on-premises operations to off-site in a few days is quite fascinating.

Although remote working placement comes with many advantages, it generates more opportunities to fall under cyber traps.

In this era, when all cyberattacks and attackers are disrupting businesses, it is prominent to take some actions to immune business infrastructure.

According to Statista’s latest survey, in 2019, the cyberattack penalty was paid around USD 40.8 Billion, and if it continues, it might grow to USD 54 Billion in the next two years. As per the forecast of Markets and Markets, the global cybersecurity market will grow from USD 152.71 billion to USD 248.6 billion at a CAGR of 10.2% from 2018 to 2023.

Before we can start securing systems, at first, it is important to gather some basic information about the following questions such as:

  • How are businesses creating their systems and resources?
  • From where they are developing all these systems resources, like from home or any public place?
  • Which platform are they using, such as online or offline?
  • Also, do they have an idea about the security of the policy of that platform?

The emergence of new technologies always brings new vulnerabilities and leads to cyberattacks, regardless of how precisely and closely you are designing security models for vulnerability management.

The biggest concern nowadays is to implement the best cybersecurity solutions, which are capable of securing business systems against modern cybersecurity threats. That’s where AI comes as a new hope to fight cyber threats.

Undoubtedly, Artificial Intelligence cybersecurity platforms are more precise and efficient towards cyber attackers, known as AI-focused security.

AI can proliferate the benefits of traditional data security systems by patching up their loopholes and supplementing them in various ways. Also, Big data is becoming a helping hand for artificial intelligence to identify contextual information regarding loopholes, malware threats, and many other threats along with ways to deal with new threats.

After doing considerable research, we have gathered these AI-based cybersecurity best practices, trends, patterns, and other insights to survive in this digital world.

Related Read: How AI & ML Helps Businesses In Risk Management

Stay One Step Ahead:

The first approach to fight against phishing and social engineering attacks is to find and eliminate problems or gaps in systems that could be the source of the attack. After the loopholes are patched up, it is important to keep the sensitive content away and secure from malicious actors.

An AI control system uses specific algorithms to identify cyber threats from phishing-related communications or frauds and monitor affected email accounts. These algorithms continuously monitor email activities and whenever they detect any malicious activity, remove them from the inbox instantly to prevent the attack.

Sustainable Security:

Business cybersecurity surveillance processes should always be proactive to protect systems from cyber-attacks. That’s where AI comes to the rescue by proactively monitoring and securing systems. To keep continuous surveillance online, the AI security system must be active all the time, especially in departments like medical and law enforcement.

Related Read: 6 Best practices for ensuring cloud security

Real-Time Prevention:

Most security solutions often come with firewall monitoring tools that can detect potential threats, anonymous & doubtful users. But they only alert the security teams after potential damage has already been done or reached its threshold level. Hence, it is recommended to enforce business infrastructure with machine learning and artificial intelligence capabilities.

Real-time monitoring systems, like AI and ML-based ones, give all data security controlling power to the cybersecurity team, helping them fight threats proactively. As compared to conventional applications, these systems are extremely predictive. For example, with every new finding, it notifies the security team or takes preset actions for familiar cases.

The famous or widely used proactive AI & ML powered data security systems are Darktrace, Paladion, Cylance, Vectra AI, and more.

Embedded Security:

Majorly, engineers first focus on application functional layers and then on security. But with new advancements, this has to evolve and bring a new security concept such as continuous protection. Now, many organizations prioritise security as the foundation of a system to embrace better efficiency and reliability. Security Engineers can implement a non-siloed and seamless platform for detecting and reacting to cyber threats in real-time. It makes systems become more precise over time as it collects more information.

Data-Driven Security:

AI cybersecurity system often comes with big data abilities and existing information. It provides complete visibility to the potential security threats through detailed reports, making it a data-driven or analytics-driven security platform. By analyzing such reports, the cybersecurity team and experts can identify the location security gaps like authentication vulnerabilities in applications and external problems in devices.


This pandemic has impacted the mental health of society and the global economy significantly by unbalancing global supply chains. The right investment in cybersecurity services may bring positive results and enable you to have a better future.

As AI incorporates new security approaches, businesses can work remotely from anywhere in the world regardless of any security threat. Still, if your organization is not cybersecurity and looking for the best cybersecurity company in India, consider CloudStakes Technology Pvt. Ltd. as the best cybersecurity partner. Contact us today to get the best cybersecurity services in India!

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