AI Prompts Us to Consider “The Future Revisited” in Cybersecurity

RevBits
4 min readDec 20, 2023
future of cybersecurity

The phrase “the future revisited” in the context of cybersecurity suggests that there is a need to reassess or reevaluate the future of cybersecurity based on new developments, emerging threats, or changing circumstances. It implies a retrospective examination of previous assumptions, strategies, or predictions regarding the future of cybersecurity in order to adapt and respond effectively to the evolving landscape.

AI in cybersecurity performs tasks that simulate human intelligence, such as making decisions, recognizing speech, perceiving visuals, and translating languages. AI can also learn from data to comprehend the context and react in different situations.

AI-enabled cybersecurity can detect new threats, generate alerts, identify new malware, and protect sensitive data. AI can also automate threat detection and response and spot malicious activities and behaviors that traditional software or manual techniques may miss.

Technologies, attack vectors, and defensive measures are constantly evolving

Over time, an enterprise’s previously anticipated or predicted state of cybersecurity may have changed or diverged from initial expectations. Therefore, it is necessary to revisit and update strategies, policies, and approaches to ensure the continued security and protection of digital systems and information.

The notion of revisiting the future of cybersecurity encompasses several aspects, including:

  • New threats and attack vectors constantly arise as technology evolves. Revisiting the future involves staying abreast of emerging threats and reevaluating security measures to address these risks effectively.
  • New technologies, such as artificial intelligence and the Internet of Things (IoT), can impact the cybersecurity landscape. Reassessment of how these advancements might affect security practices is crucial in order to adapt strategies accordingly.
  • Laws and regulations related to cybersecurity evolve over time. Revisiting the future involves considering the impact of new regulations on security practices and ensuring compliance with updated requirements.
  • Evaluating past cybersecurity incidents, breaches, and vulnerabilities can provide valuable insights. Learning from these experiences and incorporating the lessons into future strategies will enhance overall security.

Today’s cybersecurity must emphasize the need for continuous adaptation and reassessment of security measures, strategies, and technologies to effectively address emerging threats and changing circumstances in the rapidly evolving cybersecurity landscape.

RevBits EPS endpoint protection harnesses the power of machine learning and deep learning technologies

RevBits Endpoint Security (EPS) transforms your security infrastructure by empowering it like never before. With four robust datasets at its core, including separate collections for clean and malicious applications, RevBits EPS provides unparalleled asset protection. These datasets comprise both 32-bit and 64-bit versions, incorporating over 2 million samples and an impressive combined data size of 6 terabytes.

The true strength of RevBits EPS lies in its continuously evolving machine learning and deep learning models. Through regular updates, it stays ahead of emerging threats and ensures the utmost protection for your assets. At the heart of its deep learning models, we have expertly crafted 866 unique features within an artificial neural network boasting four layers. The final decision-making process resides in the output layer, which houses a single neuron. This meticulous analysis of various application aspects, such as code, text, data sections, binary files, header sizes, resources, and debug sections, achieves a remarkable level of accuracy. By extracting and processing these features, RevBits EPS effectively differentiates between clean and malicious apps, harnessing the power of our neural network and deep learning models.

To guarantee optimal performance, the machine learning and deep learning models undergo extensive training utilizing 80 terabytes of storage. This training is powered by multiple NVIDIA Quadro RTX 8000 and Tesla TR GPUs, specifically designed for memory-intensive workloads. These high-performance GPUs enable RevBits EPS to create complex models, build vast architectural datasets, and effortlessly visualize immense data science workloads.

RevBits protects organizations with a unified cybersecurity architecture

RevBits unified multi-layered security advances cybersecurity to a new level by taking down security barriers that formerly challenged enterprises. RevBits solves serious problems created by siloed security products that cause security gaps, leaving enterprises vulnerable to myriad cyber-attacks. RevBits automates the detection and remediation of anomalous activity across a cross-functional security stack. Coalescing them into a single intuitive GUI dashboard, RevBits enables rapid cyber forensics with analytics and context, to quickly resolve threats.

Learn how RevBits cybersecurity products can be your best defense for protecting corporate assets today and into the future.

Originally Published on www.revbits.com

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RevBits

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