CYBER SECURITY
CYBER SECURITY
NETWORK INFRASTRUCTURE
TRANSFORMATION
Data loss prevention (DLP) is essential for all industries' data security strategy. Linearstack recommend it in highly regulated sectors, and necessary for compliance along with stopping insider threats. However, not all DLP solutions are the same. It is important to remember that DLP has existed since the mid-90s, and the workplace has undergone significant changes since then.
Artificial intelligence (AI) and machine learning algorithms (ML) have become a positive force in the DLP space. Current implementations of DLP continue to be expensive and not delivering expected security protection results.
Organisations needing more internal resources to staff their security teams to help design, deploy, and monitorAI-based DLP solutions should work with cybersecurity consulting firms like Linearstack.
This New Zealand-based cybersecurity consulting and managed security service provider (MSSP) brings several years of experience in the DLP space. The firm helps organisations develop and manage their DLP strategy across every aspect of the enterprise, including email security, endpoint DLP, and host-based DLP solutions. Linearstack also assists clients in better understanding the value of AI and ML to better detect malicious activities and potential threats and provide accurate data reporting to make informed decisions regarding how to respond to compliance breaches.
Your organisation generates significant unstructured data, including sensitive information, trade secrets, and personally identifiable information (PII). Next-generation, AI-enabled Data Loss Prevention (DLP) has emerged to address the need to centralize DLP management across the entire enterprise.
AI and ML are part of the software tools better at predicting without additional programming, abnormal user behavior, inevitable risk, and catching things human analysts may have missed. Giving more historical data improves the tool's predictions and finds hidden trends—AI-driven data security products benefit by feeding more data into their intelligent algorithms. The output from these algorithms helps the organisation shift from a reactive to a more proactive security strategy.
ML helps identify and classify sensitive and high-risk data resources. AI-powered DLP solutions, powered by ML algorithms, can quickly organize data elements as they are created and introduced, along with faster decisions to stop ransomware attackers from propagating across the network and cloud computing environments.
AI-powered DLP is superior and faster at identifying sensitive business data than old solutions. Plus, it'sself-learning, reducing the need for IT intervention and allowing them to focus on more critical tasks. Legacy DLP solutions require an updated download of new rules to stay current with the latest threats. AI learns continuously without the need to make manual updates.
Many compliance mandates recommend DLP as part of the security protection requirements, including HIPAA, PCI, and NIST frameworks. DLP helps reduce the potential damage caused by bad actors accessing corporate networks through anomalous activity. AI and ML make DLP much easier to manage while increasing its effectiveness against the wrong person breaching the organisation.
While AI and ML bring great promise to DLP, these innovative solutions take effort to implement. AL and ML require data to process over some time as part of the Large Language Model (LLM). LLM is acritical component of the learning phase for AI to develop the datasets and trends before passing the results into the ML process.
Within this learning period, basic DLP rules and static configurations will bring various levels of protection capability. Until the LLM completes its initial phase, AI will not be a protective layer within the DLP solution.
AI and ML can help your DLP solution find and secure sensitive data. The machine learning component learns from new data and redacts sensitive information automatically. ML also analyzes user behavior, allowing the DLP solution to respond to risky or abnormal behavior by redacting or blocking information.
AL and ML within DLP also reduce false positives and rationalize regular activity faster through continuous learning.
AI datasets and ML algorithms can help enforce data handling policies. A DLP tool learns from past incidents to protect data proactively. For example, it can block attempts to email sensitive materials and encrypt files based on how the information became used in the past. It can also take other preventive actions based on company policies.
The detection of corrupt data and suspicious activity, which was previously difficult and time-consuming with traditional DLP policy-based rules, has become more straightforward with AI. Previous data attacks are automatically recorded and considered in futuredecision-making within the AI scope.
AI/ML-driven DLP solutions can block or exclude high-risk users based on usage and behavior. DLP solutions scan data patterns in real-time across different areas, allowing companies to focus on enhancing security and reducing costs. Previous DLP solutions required much time and money to monitor and update.
MSSPs like Linearstack understand the complexity of DLP and AI. Both technologies require higher expertise and experience in security operations monitoring, incident response, and ongoing adjustments. AI-powered DLP, however, helps reduce the management overhead; however, this solution still needs to be monitored. LinearStack’s 24x7Cyber Vigilance helps clients reduce the monitoring cost while freeing internal resources to focus on more strategic projects.
Another essential value Linearstack brings clients is their expertise in getting the most out of cybersecurity investments. Organisations that spend money on the latest security architectures, including extended detection and response (XDR), zero-trust, and DLP, must realize how much overlap will exist. Its initial assessment engagement process is a significant component of Linearstack's service value.
Linearstack leverages assessment engagements to help determine which cybersecurity adaptive controls will best serve the client's needs based on the current and future threat landscape, available budget, and compliance mandates. These assessments help the organisation champions position this solution with their CIO, CISO, CFO, and CEO to gain executive sponsorship and continuous funding support.
Linearstack is a leading Managed Security Service Provider (MSSP) and security systems integrator based in New Zealand. Since our establishment in 2013, we have built a reputation for providing world-class 24x7 security services to businesses of all sizes. We are proud to partner with some of the top technology companies in the industry, such as Palo Alto Networks, Cisco Systems, Imperva, and LogRhythm. Our excellent operational capabilities, as well as our fulfillment of business requirements and completion of rigorous technical, sales enablement, and specialization examinations, have earned us a distinguished reputation in the industry.
At Linearstack, we take pride in providing top-notch security solutions tailored to our client's needs. We aim to help businesses reduce cyber-attack risks, strengthen security posture, and maintain regulatory compliance. Our clients rely on us for our exceptional security solutions, outstanding customer service, and industry expertise.
We’re 100% privately held, grown with a family mindset. When working with clients, we’re well-integrated within their teams and act as an extension of their operations. Augmenting existing teams isa transition we manage smoothly, empowering our customers to prioritize cybersecurity strategy while we protect their business from cyber threats 24x7.
Maintaining thriving IT systems and assuring data protection are fundamental needs that all businesses deserve.
Want to know more about what we offer? We'd love to hear from you.
Phone: 0800 008 795
Email: info@Linearstack.co.nz
Website: https://Linearstack.co.nz
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