Splunk User Behavior Analytics and Palo Alto Networks URL Filtering with PAN-DB compete in the cybersecurity field, offering complementary solutions. Palo Alto stands out for comprehensive filtering features and ease of deployment.
Features: Splunk User Behavior Analytics focuses on anomaly detection, user behavior analysis, and real-time insights into security threats through machine learning. Palo Alto Networks URL Filtering with PAN-DB provides strong category-based filtering, credential phishing protection, and real-time threat updates integrated with WildFire.
Room for Improvement: Splunk may benefit from simplified deployment, better out-of-box configurations, and enhanced user interface navigation. Palo Alto could improve by reducing manual policy updates, enhancing documentation clarity, and refining the user feedback mechanisms for new categories.
Ease of Deployment and Customer Service: Palo Alto Networks URL Filtering with PAN-DB is recognized for its straightforward deployment and efficient customer support, facilitating quick setup and issue resolution. Splunk User Behavior Analytics, while rich in features, requires careful tuning and setup, though it offers reliable support to assist with deployment challenges.
Pricing and ROI: Splunk User Behavior Analytics involves a higher initial investment but provides significant ROI through improved threat detection. Palo Alto Networks URL Filtering with PAN-DB offers a balanced cost-benefit with effective URL filtering capabilities, making it attractive for organizations seeking uncomplicated internet security solutions over detailed behavioral analysis.
Palo Alto Networks URL Filtering with PAN-DB is an advanced URL filtering solution that provides a way to control web access, as well as how users interact with online content. With this solution, your organization can prevent credential phishing theft by assuming strict control over which sites users can enter their corporate credentials into. Palo Alto Networks Advanced URL Filtering with PAN-DB provides web protection by using URL database capabilities to help you automatically detect and prevent new malicious and targeted web-based threats instantly.
Palo Alto Networks URL Filtering with PAN-DB Features
Palo Alto Networks URL Filtering with PAN-DB has many valuable key features. Some of the most useful ones include:
Palo Alto Networks URL Filtering with PAN-DB Benefits
There are many benefits to implementing Palo Alto Networks URL Filtering with PAN-DB. Some of the biggest advantages the solution offers include:
Reviews from Real Users
Below are some reviews and helpful feedback written by PeerSpot users currently using the Palo Alto Networks URL Filtering with PAN-DB solution.
Consultant Michael V. says, “What I really like about PAN and what makes it a worthwhile solution is that rather than having an administrator constantly updating a list of prohibited URLs, you can do it by categories. Every one of these URLs gets meta-tagged as hate speech or antisemitic or pornography, or whatever it is, and when you set up the filter everything that's prohibited by HR policy is there. I don't have to maintain anything, it catches everything.”
PeerSpot user Darshil S., Consultant at a tech services company, mentions, “The multiple categorizations of URLs are quite helpful. For example, if a URL is a social media website, such as facebook.com, it can be classified at a certain risk level - from high to low.”
Karthikeyan S., Sr. Cloud Data Architect at Sun Cloud LLC, explains, "Palo Alto Networks URL Filtering with PAN-DB is easy to use, easy to operate, and easy to edit."
Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
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