Cybersecurity enhanced with AI and ML: Improving data loss prevention
The vast and growing amounts of data being created, collected, and used by the enterprise makes the deployment of data security solutions a business imperative. It is essential to implement cybersecurity solutions and practices to prevent data leaks and breaches, but how do businesses stay ahead of the growing sophistication of cyberattacks?
Predictive technologies, such as artificial intelligence (AI) and machine learning (ML) can enhance traditional data loss prevention (DLP) solutions to greatly reduce the risk of breaches or leaks.
AI can provide critical analysis, and ML uses algorithms to learn from data—both provide a dynamic framework to predict and solve data security problems before they occur. The more data patterns ML analyses, the more processes and self-adjustments can operate based on those learned patterns. This continuous delivery of insights increases in value with the “intelligence” of the technology.
So, what are some of the advantages of implementing ML/AI into a DLP solution across the enterprise? Let’s take a look:
Businesses often use employees to handle the more sophisticated security decisions that their DLP solution cannot. Adding AI/ML to the DLP solution creates even greater efficiency by automating a higher level of decision making. Employees are freed to focus on only the truly critical or complex threats requiring an ethical approach—especially when the system has doubts, or decisions need to be overridden. Analysts can prioritise workload and complete tasks more efficiently.
The aim of deploying AI/ML to a DLP solution is not for technology to replace human analysts, but rather for technology to work in collaboration with humans to actively enhance the other’s corresponding strengths. Businesses utilise the skills of AI/ML experts to select, build, and apply data-driven innovation methodologies and solutions that go in conjunction with traditional DLP solutions already in place.
Efficiency and simplicity
The identification of corrupt data and/or suspicious activity, which was more challenging and time-consuming with traditional policy-based rules, has now become easier with AI as records of previous data attacks are automatically logged and factored into future decision making.
AI/ML-driven DLP solutions can automatically block or shut out a specific high-risk user based on usage or behavioral patterns, preventing a data breach or leak. Data patterns can also be scanned across geographies, departments, or processes in real time, allowing companies to identify weak areas and focus efforts on bolstering security in these areas.
Handling big data
AI and ML can sift through and analyse massive amounts of data simultaneously to detect threat events faster and more accurately than any human or traditional DLP solution. In fact, the more data available, the more patterns AI/ML can detect and learn from to spot abnormal activity (or cyber threats) in the normal pattern flow.
AI/ML tracks user activity considered to be normal, such as the login time and location of a person’s device, the activity of that device, and more. Any aberrations from the user’s normal activity, such as logging in from a different location, get flagged instantly and can be blocked or suspended until verification can be provided. This new information can then be factored into the user’s future behavior.
Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located IoT Tech Expo, Blockchain Expo, AI & Big Data Expo and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam and explore the future of enterprise technology.
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