Reduce the Harm of a Data Breach With Data Security Analytics

Over the last year, the average total cost of a data breach increased nearly 10% to $4.24 million. That’s the highest average in the history of IBM Security’s annual Cost of a Data Breach Report. This was due to a number of factors. Increased remote working due to the COVID-19 pandemic and digital transformation both matter. Critical industries struggled to adapt their cybersecurity programs to the changing data landscape and IT infrastructure. Now, they need to consider security tools that are adaptable, intelligent and connected. Automation is one lever to pull in the fight against data breaches.

What Is Security Automation?  

Security automation means tools that augment or replace human work in the detection and containment of attacks and intrusion attempts. This includes solutions that depend on artificial intelligence (AI), machine learning, security analytics and automated security orchestration.

In the survey, respondents with a mature use of security analytics saw data breach costs 32.9% lower than for organizations with less mature programs. Security AI and automation can also significantly reduce the average time to detect and respond to a data breach. This, in turn, leads to lower average costs.

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Data Security Analytics Uncover Potential Threats and Risky Users

Key data security solutions can help discover and classify sensitive data on-premises and in the cloud. They can also monitor data activity for policy violations and detect unusual user data access. But a superior solution takes this one step further with smart and connected features. These built-in features help uncover the highest risks and strange behaviors. What’s more, they seamlessly create tickets for the security operations center (SOC) to find and fix.

Whether or not they’re facing an active data breach, security teams wade a huge volume of noise and alerts. So, you need to empower them to quickly discern and focus on the most significant threats in real-time. To do so, they need at-a-glance risk-based views. Those call out high-risk areas. From there, analysts can click through, read about them and drill down to see more. Advanced analytics can help automate threat hunting and prioritization, too. It includes sequenced-based analytics, outlier detection analytics, risk spotting algorithms and threat detection analytics. This built-in threat intelligence can help streamline detection and investigation. That way, your team is only spending their time on the most important tasks.

Preventing a Data Breach or Insider Threats With Security Analytics

Preventing insider threats is a critical use case, especially for a remote workforce. Data analytics can help score risky users to flag for a closer look. Until that look is complete, database admins can put in place policies to redact data from view as needed. Or, they can take action right away by blocking user access. What’s more, you can extend those protections to address data privacy rules and zero trust.

Whether you’re facing a data breach or something else, it’s helpful to break down silos and speed up response workflows to reduce risk to the business. So, look for solutions with pre-built integrations and open application programming interfaces (APIs). These make it easier to talk across teams and tools. The best way to reduce the time to address incidents is by making it simple to open tickets. It also needs to be simple to share insights that can enrich SIEM and SOC playbooks. Automation, process standardization and integration can all make incident response faster. And all of those can help reduce the total cost of a data breach.

Data Security Aligns With Zero Trust

Data security with an emphasis on data loss prevention and access control will help with something else, too. It’s part of a zero trust model for cybersecurity. A zero trust approach operates on the assumption that user IDs and network traffic may already be compromised. Instead of trusting them, it relies on AI and analytics to constantly validate connections between users, data and resources. Today, many workplaces are shifting to remote work and have become less connected. A zero trust strategy can help protect data and resources by making them accessible only on a limited basis and in the right context to prevent and minimize a data breach or other cyberattacks. That context and control are powered by a strong data security program.

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