Security risk is everywhere and it has been growing rapidly while we try to mitigate the threat at the same time. The fraudsters are always a step ahead of the curve and come up with new ideas for attacks while we are busy handling the older ones. The mitigation of security risks requires all of us to innovate faster and prepare in a much more advance and modern manner. Most of the time we keep solving the older problems due to the enormity of challenges here and forget about preparing for the potential upcoming attacks. The sheer definition of the problem is not available most of the time, the tools in the market are sparse and siloed, and the concepts are available but implementations are limited. The core of the solution lies in the ability to scan every single event in context and use modern methods to not only do forensic but be predictive to avoid the repercussions
The cybersecurity threats have changed in three crucial ways in the recent past:
- MOTIVE: In the past viruses were introduced by curious programmers. Today cyberattacks are a result of a well-executed plan by trained militaries in support of cyber warfare.
- SPEED: The potential rate at which the attack spreads has also increased and can affect computers all over the globe in seconds.
- IMPACT: the potential impact has increased manifold due to the wide penetration of the internet
Continuous and relentless: Threats may come from any place, any system, and the most unlikely of places. Therefore, we must capture and analyze all data (and not just samples). Hence stream processing in a continuous manner is critical where all events/data are analyzed with as low latency as possible. Most of the tools in the market are batch processing tools, they miss the pattern at the boundaries of the batches, therefore not suitable for such use cases
Non-atomic in nature: Thread may not necessarily be atomic in nature; it may arrive in small packets over a period from many different sources. Therefore, by just looking at a single packet or event we can’t perceive the thread. We must analyze these arriving data packets in the state-managed system with a continuously moving window that can see the pattern over a period. Also, we need to link data points to capture the essence and context
Unpredictability: Few threats may have known or constant signatures, which we could identify using some computations in the deterministic and absolute manner. However, several threats are extremely hard to be captured in this way as they are designed to miss the regular known or anticipated structure. Therefore, we must use AI to predict some of these scenarios continuously on stream data
High-speed processing: The speed of data is so high in some cases that existing tools in the market would sample and then process. We know that this is too open and risky. We must capture and process all data. This means we must have a system that has very high performance in reading and writing. The high throughput data store is desired in such cases
Linear scale: Data volume would be high as we need to process and store all data. A large scalable system would be needed to achieve this. We need a linear scale to ensure the data ingestion and processing work uninterrupted while system scales
What BangDB does do? – It enables a Predictive instead of a Forensic approach with high speed in a scalable manner
BangDB ingests and processes the security telemetry information at extremely high speed to make it easily accessible for advanced computation and analytics. It further leverages the following to achieve a predictive vs forensic approach
- Advanced statistical and data science models for high-speed anomaly detection
- Real-time ingestion and stream processing to enable continuous threat analysis
- Machine learning models integrated with stream for predictive threat detection
- Graph with stream for interlinking of data points for richer pattern detection
- Handle all kinds of data in a single place, text, images, videos in a joined manner
What are the typical steps that BangDB takes to tackle this?
STEP1: Advanced Threat Detection
Need to leverage BANGDB to combine and contextualize incidents from multiple big data disparate sources for continuous near real-time streaming detection, capturing incidents that are often missed in batch-based technologies.
STEP2: Link data
Enrich data with a Graph model to capture the “context” rather than just isolated events which do not provide enough information. Further, integrate Graph with Stream processing such that the linking of data and context capturing in automated and continuous
STEP3: Complex event processing
Find anomalies, and patterns using complex event processing (CEP). This allows users to define a certain pattern that is so complex in nature that can’t be run on typical RDBMS or other databases. The pattern identified here are absolute in nature and with 100% confidence
STEP4: Predict and don’t depend on forensics as much as possible
Artificial intelligence enables the identification of never seen threats, malware, and infiltration techniques. Using AI, build a comprehensive security score leveraging behavioral modeling and stochastic anomaly detection. Kill chain incidents are prioritized based on potential impact, key users, and critical assets.
STEP5: Take automated action
When an anomaly or pattern is detected, take action in an automated manner. This means timely action which could potentially result in saving time and resources and in many cases avoiding the situation itself
STEP6: Track threat propagation
Leverage BangDB’s ability to ingest and analyze immense amounts of data to track threats and their propagation across time and space through a near real-time relational-graph view of the entire network
STEP7: Visual threat hunting
Sophisticated threat hunting tools within the Security Intelligence platform to allow the SOC staff to effectively hunt, validate and remediate potential threat incidents surfaced by the product. Analysts can self-assemble new threat hunting workflows using building block modules for ingestion, enrichment, and analytics on a security playbook interface.
In the end, no amount of effort and tools can make us completely insulated from these security threats, there is no complete immunity that we can develop for such things. However, we can at best be prepared for such attacks and try to avoid them as much as possible. And in case of some attacks, we can try and minimize the damage. An additional set of tools would never hurt, they can probably add more value and make the situation better, hence it is recommended to try BangDB to make the castle bit more impregnable
Download BangDB for free and get started, BangDB is one of the “fastest databases” in the market. It performs 2X+ when compared to some of the most popular databases like MongoDB, Couchbase, or Redis.
Please see more related articles on the following;
- The hidden benefits of NoSQL Architecture
- Why AI is needed within the database
- Why A Novel Data Processing Philosophy Is Necessary For An Emerging Data Trend