Database Cyber Security Guard detects Zero Day, Data Breach and Ransomware Attacks in milli-seconds with Artificial Intelligence and Deep Packet Inspection. Would have detected the LOG4J and SUNBURST data breaches immediately. Protects data as organizations move to the cloud. SQL activity from anomalous IP addresses are immediately detected.
November, 2024. High-Severity Flaw in PostgreSQL Allows Hackers to Exploit Environment Variables.
October, 2024. Fidelity Investments data breach. 77,000 social security and drivers license numbers were stolen by Hackers.
August, 2024. Microsoft issues patches for 90 security fixes, including 10 critical Zero-Days.
June, 2024. PostgreSQL vulnerability allows Hackers to bypass multi-factor authentication to issue unauthorized SQL queries.
May, 2024. Dell warns of data breach, 49 million customers allegedly affected.
December. 2023. MongoDB announced its corporate databases were breached and that customer data was accessed by Hackers. The unauthorized access had been going on for some time before being discovered.
September, 2023. MSSQL databases hacked by DB#JAMMER. After infiltration Hackers expand their foothold within the target server and use MSSQL as a beachhead to launch several different payloads.
2021. Morgan Stanley disclosed a breach where customer names, addresses and social security numbers were stolen. Data was encrypted however Hackers were able to obtain the decryption key to unencrypt the database data.
The Database Cyber Security Guard informs Security Professionals and DBAs of Zero Day, Ransomware and Data Breach attacks within milli-seconds when Hacker, Rogue Insider, Supply Chain, 3rd Party Cyber Risk, Phishing Email, Dev Ops Exploit and SQL Injection Attacks occur. A next generation approach to Data Loss Prevention (DLP).
Protects credit card, tax ID, medical, social media, corporate, manufacturing, trade secrets, law enforcement, defense, homeland security, power grid and public utility data. Supports key GDPR compliance requirements.
Product is a User and Entity Behavior Analytics (UEBA) cyber security solution using Advanced SQL Behavioral Analysis Analytics. Performs real-time Data Loss Prevention (DLP) using Machine Learning and Deep Packet Inspection of 100% of the database network packets. Does not need to connect to the protected database servers. Protects 24×7 all data in DB2, Informix, MariaDB, MongoDB, MySQL, Oracle, PostgreSQL, SQL Server and SAP Sybase cloud and on-premises databases.
Zero Impact Sql Capture Agent and Agentless Monitoring of SQL Wait Times products.
Be informed of long running SQL within milli seconds of its occurrence. Product uses non-intrusive Network Sniffing and Deep Packet Inspection (DPI) to capture/monitor 100% of the SQL activity 7×24 with its SQL text, end-user response time and much more. 95% of database performance issues are due to long running SQL that the product immediately pinpoints using DPI.
Unlike Agentless Monitoring solutions that miss monitoring ~95% of OLTP application SQL since they only sample the server activity once every N seconds – the Zero Impact Sql Capture Agent monitors/captures the performance of 100% of the SQL activity with no impact on the monitored servers. Agentless Monitoring solutions are helpful when monitoring high level server resource usage such as CPU, memory, disk, etc. However not that helpful in detecting/monitoring with 99.9% accuracy poor OLTP SQL performance (i.e. SQL requests with degraded or poor end-user response time).
The Zero Impact Sql Capture Agent also baselines the performance of every unique SQL request 7×24. Proprietary anomaly detection identifies SQL requests running longer than their prior performance baselines. Most servers have 2K to 20K unique SQL requests that run millions of times a day. Available for DB2, Informix, MariaDB, MySQL, Oracle, PostgreSQL, SQL Server and SAP Sybase.
Our companion Agentless Monitoring solution also monitors SQL Wait Times. SQL Wait Time monitoring is helpful when monitoring data warehousing or data mining SQL where the SQL typically runs longer than 1 or 2 seconds. Drill into SQL wait conditions, SQL wait times, I/O stall times, blocking, deadlocks, performance counters, disk usage, index fragmentation and SQL agent job performance. 3D graphs of SQL wait times, I/O stall times, performance counters, procedure cache, buffer cache and disk usage. Automatically identifies poor SQL plans for SQL having high wait times.
No intrusive database SQL profilers or traces are used. View a summary of server farm performance in dashboards and heatmaps. Has no impact on database servers or the servers they run on. Does not miss incidents of long running SQL that agentless monitoring solutions miss since they typically investigate for poor performance only every N seconds.
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