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Yariv Fishman, Deep Instinct's Chief Product Officer, defines preemptive data security and explains why it's so important in preparing to combat the looming AI-powered threat landscape.

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Cybercrime and cybersecurity are in a constant arms race. Over the years, attackers and defenders have&nbsp; escalated their battle with new methods and technologies, each vying for an advantage. We are now at the precipice of cybercrime’s most dangerous evolution, which has the potential to deliver a knockout blow—Artificial intelligence (AI) models designed without safeguards, or so-called <a href="https://www.deepinstinct.com/cyber-threat-reports">Dark AI, are supercharging attackers’ capabilities</a>. These Dark AI models are on the cusp of running sophisticated attacks that can easily bypass existing security tools, an eventuality for which most of the cybersecurity community isn’t prepared.In recognition of this growing threat, <a href="https://www.gartner.com/en/documents/5818147">Gartner released research</a> detailing a new category of cybersecurity, defined by its ability to prevent threats before they execute, adapt to new threats, and scale to the size and speed required by governments and enterprises. Gartner calls this new category <a href="https://www.gartner.com/en/documents/6318147">Preemptive Cybersecurity</a> and predicts that it will be an operational requirement by the early 2030s, releasing broad definitions of what preemptive solutions entail.This blog goes further and defines the key tenets and requirements of Preemptive Data Security, a specific implementation of Preemptive Cybersecurity. A true preemptive data security solution must have all of these qualities:<h2>Effective Unknown Threat Identification</h2>Definition: The ability to determine the malicious nature of a file by its intrinsic behaviors and characteristics, not by pattern matching against a database of known threats.&nbsp;Preemptive data security relies on several unique functions operating together to be effective. Most importantly, a preemptive solution requires the ability to identify unknown and zero-day threats.Unknown threat identification matters because Dark AI tools are complicating how attacks unfold. In a paradigm shift that will rock much of the cybersecurity industry, <a href="https://www.deepinstinct.com/blog/beyond-flesh-and-code-building-an-llm-based-attack-lifecycle-with-a-self-guided-agent">malware is now being written in minutes and permuted</a> in just seconds alongside more effective obfuscation strategies. Non-adaptive defenses that rely on signatures, CVEs, and threat feeds to remain effective will be fighting blind as their efficacy craters.To combat this shift, preemptive solutions need to be able to autonomously and intelligently identify never-before-seen threats. New attacks are unfolding with alarming speed, overwhelming <a href="https://www.deepinstinct.com/deep-instinct-vs-competitors">legacy cybersecurity tools</a> that lack the ability to defend against novel threats. Defenses need to be smarter.<h2>Real-Time Zero-Day Prevention</h2>Definition: The ability to recognize never-before-seen or encountered files as malicious and automatically quarantine or delete them pre-execution, without disrupting the flow of data through an organization.Alongside unknown and zero-day threat identification is the ability to prevent threats before they execute. That means that the identification process must be completed before a file is written to disk or uploaded to the cloud—and the solution must be capable of quarantining or deleting files instantaneously. Merely seeing a threat coming isn’t enough to prevent it. The preemptive solutions of the future will have the autonomy to make real-time decisions and protect organizations seamlessly.Because <a href="https://www.deepinstinct.com/blog/the-rise-of-ai-driven-cyber-attacks-how-llms-are-reshaping-the-threat-landscape">Dark AI is making attacks easier to launch and reinforce</a>, the volume will continue to grow. Effective defenses require a solution that can match the increased volume, identifying malicious files and automatically preventing them from breaching their targets.<h2>Real-Time Insights and Explainability</h2>Definition: The ability to scan malicious files and provide, in moments, a detailed readout of the characteristics of the planned attack, including capabilities, obfuscation methods, and any other available information.Detection and response only solves part of the problem. SOC teams must understand more than just the how, but the why. They are already overwhelmed by the scale of daily alerts, <a href="https://www.deepinstinct.com/pdf/infographic-voice-of-secops-5th-edition-ai-in-cybersecurity-friend-or-foe">with 56% of SecOps teams reporting an increase in stress YoY</a>. Increased attacks generated by Dark AI are only worsening the problem. SOC teams are still responsible for investigating incoming attacks and determining as much as possible about them. Merely preventing attacks doesn’t help them understand the big picture, which can leave them vulnerable to future attacks. Empowering human security teams requires real-time insights and explainability into why an attack was prevented and which vulnerabilities it sought to exploit.<h2>Enterprise Speed &amp; Scalability</h2>Definition: The ability to seamlessly adapt to growing asset and data estates, including additional endpoints, configurations, and storage methods, without incurring speed-based bottlenecks or introducing unsustainable infrastructure growth.The amount of data produced and stored by organizations is exploding. Nearly every device is now connected in some way or another. Assets that form vast IoT networks are becoming more commonplace, while portals that exist outside of organizational perimeters make it easier than ever for customers and clients to upload data and communicate. With these advances comes additional vulnerabilities; weaknesses that criminals target using new methods that <a href="https://www.deepinstinct.com/endpoint-detection-response">bypass traditional endpoint protection</a>.Preemptive data security solutions must be able to adapt to expanding data ingestion, communication, and storage capabilities to ensure that organizations can operate and grow without introducing new vulnerabilities. This necessitates the use of streamlined models that can quickly scan every incoming file and be easily implemented into a growing infrastructure. Ease and flexibility of implementation are essential, and must be further enabled by future-proofed AI models that help to avoid the accumulation of technical debt or entrenchment.<h2>Defense Across the Entire Data Estate</h2>Definition: The ability to prevent threats wherever they are encountered in a data estate, whether at the endpoint, in applications, or in NAS or cloud storage repositories.Preemptive solutions need to work across an organization’s entire data estate to provide comprehensive, unified protection. Many of the same reasons that make scalability and speed a requirement also apply to the need for whole-estate data protection. Additional connection points, growing storage, larger device fleets, and portals all need to be visible and protected from incoming malicious files.Flexible, unified solutions as part of a defense-in-depth strategy allow for more comprehensive, layered data protection. Integrating into different aspects of the data estate and adapting to incoming threats is an important capability for preemptive solutions because the scope of cyberattacks has expanded dramatically. Single-point solutions like EDRs may be suitable for protecting their small domain, but the reality is that the attack surface has expanded well beyond their scope.<h2>Purpose-Built Deep Learning Framework</h2>Definition: Deep learning is the most advanced form of artificial intelligence. A purpose-built deep learning framework is trained on millions to billions of data points to teach it to recognize threats faster and more accurately than any other solution on the market today.Bringing together all the tenets above requires a deep learning (DL) framework. Machine learning (ML) frameworks are too brittle and myopic to provide any of the required capabilities, especially as they grow in complexity. They fall especially short of preventing unknown threats.The power of DL stems from its ability to continuously learn and adapt, making autonomous connections as it encounters more data. In turn, it grows in efficacy against unknown and zero-day threats over time and can be integrated across data estates. Additionally, the speed with which DL models can ‘think’ and make decisions far surpasses the speed of ML and analog models, which, in the case of cybersecurity, allows it to scan files at much greater speed and volume with a much smaller footprint.And, because DL models don’t rely on comparative analysis to determine if a file is malicious, they can explain why files are quarantined or prevented. Utilized alongside a generative AI model that can translate results into plain language, true explainability can be achieved, augmenting human teams and speeding the investigation and remediation process while reducing burnout.<h2>Next Steps</h2>DL models don’t grow on trees. In fact, there are only around a dozen publicly known DL frameworks in the world—and 11 have nothing to do with cybersecurity. Deep Instinct’s <a href="https://www.deepinstinct.com/dsx/brain">DSX Brain</a> is the only purpose-built deep learning framework for cybersecurity and the only solution positioned to meet the present and future needs of organizations looking to implement preemptive data security in their environments.Cybercrime isn’t waiting for defenders to catch up. When AI saturation happens (sooner rather than later), traditional defenses are going to collapse. The legacy players in the cybersecurity space have not built the technological foundation necessary to transition to preemptive security. Training models that can prevent threats require a huge investment in AI researchers, hardware, data, and time. Unfortunately, time is the rarest commodity on that list.Deep Instinct is built to fight the future. We provide better security with better AI. We fight Dark AI with better AI. And we are capable of providing the capabilities that comprise a true preemptive data security solution today.Try Deep Instinct now; <a href="https://www.deepinstinct.com/free-scan">request your free scan</a>. Put us to the test.

The Future Has Arrived: Defining Preemptive Data Security

As the cybersecurity industry plays catch up, Deep Learning continues to be the only way to predict and prevent sophisticated AI threats.

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With the widespread availability of generative AI, new cyber threats are emerging daily. Dark AI tools like <a href="https://thehackernews.com/2023/07/new-ai-tool-fraudgpt-emerges-tailored.html" target="_blank">FraudGPT</a> and <a href="https://thehackernews.com/2023/07/wormgpt-new-ai-tool-allows.html" target="_blank">WormGPT</a> have opened advanced adversarial attack methods to a broader audience of less sophisticated actors.The threat landscape has fundamentally changed.As a result, traditional cybersecurity measures no longer work. Organizations need something more powerful and advanced to stay ahead of cybercriminals, to anticipate their next move before they make it, and to prevent ransomware, unknown, and zero-day threats from landing in the first place.Only deep learning has the power to match the speed and agility of this next-gen adversarial AI.There are less than 10 deep learning frameworks in the world – despite other cybersecurity vendors’ bold, false claims on their promises of AI protection. And there’s only one <a href="https://www.deepinstinct.com/why-deep-instinct?_ga=2.237779200.1255878901.1702848544-667067167.1702335063" target="_self">deep learning model dedicated to cybersecurity</a>: Deep Instinct.The power of Deep Instinct rests in our ability to understand and identify the DNA of an attack – detecting and stopping it before it can execute on an endpoint. Its ability to self-learn is extraordinary. It is unmatched in its speed and accuracy to autonomously predict, detect, and prevent threats.We’re fighting dark AI with deep learning.In contrast, most traditional cybersecurity vendors are attempting to advance their machine learning models to fight back against emerging AI-based threats. But not all AI is created equal. You can’t fight advanced adversarial AI with basic machine learning, the technology most cybersecurity vendors hang their hats on. It’s the most basic form of AI and is unsuitable for the task.On a recent <a href="https://www.cnbc.com/2023/11/29/crowdstrike-ceo-says-ai-can-help-prevent-ransomware-attacks.html" target="_self">Mad Money segment, CrowdStike’s CEO George Kurtz told Jim Cramer</a> that using AI to fight sophisticated ransomware attacks is critical to combat the growing threat of dark AI.To address it, Kurtz pointed to the important need for prevention-first technologies that don’t rely on traditional signatures. And he reminded listeners that CrowdStrike has incorporated AI into its product for years.But here’s the catch: the AI advancements from IT security vendors like CrowdStrike are more of the same – they’re still fundamentally based on basic machine learning models and are, therefore, incapable of stopping rapidly evolving, AI-powered cyber threats in the wild.To counter today’s advanced adversarial AI, you need deep learning, the most advanced form of AI. We’re playing chess while other vendors are playing checkers.To learn more, I’d welcome you to check out the following:<ol><li>Read about the <a href="https://www.deepinstinct.com/glossary/deep-learning" target="_self">power of deep learning</a>, and why it is our core differentiator.</li><li>Listen to Deep Instinct’s CEO, Lane Bess, talking about this firsthand with Steven Rosenbush at the <a href="https://www.deepinstinct.com/videos?wchannelid=ysjgh8jbc8&wmediaid=miqn8ruai3" target="_self">Wall Street Journal CIO Network Summit in this video</a>.</li><li>Download our most recent <a href="https://www.deepinstinct.com/cyber-threat-reports" target="_self">2023 Mid-Year Threat Report</a>, which includes predictions about the growing threat of LLMs.</li></ol>Don’t base your organization’s security posture on false promises, or legacy vendors who are still in “catch up” mode.<a href="https://www.deepinstinct.com/contact-us" target="_self">Contact us</a> to learn how deep learning-powered predictive prevention can help your organization stay ahead of adversarial AI, during a time when it’s needed most.

Are Your Cybersecurity Vendors Really Fighting AI with AI?

How we’re protecting organizations’ most valuable asset 

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As artificial intelligence (AI) transforms nearly every industry, it’s also changing how cybercriminals approach their work.Threats have steadily become more sophisticated and commonplace, and fresh avenues for attacks are becoming more accessible for organized cybercrime. AI is arming hackers, nation-state actors, and fraudsters with means to carry out complex and calculated attacks faster, more efficiently, and more easily than ever before.All of this is occurring with more data than ever before. The average large enterprise has between seven and 17 petabytes of data stored in hybrid, cloud, or on-prem databases. With more data available – and vulnerable – and with AI-enabled tools that help cybercriminals create malware and malicious code, it’s more critical than ever for organizations to protect stored data.Breaches can cost millions of dollars to remediate, erode corporate reputation, send share prices tumbling, and anger customers. For enterprises to effectively safeguard critical data, preventing malicious files from landing in their storage is imperative. In an ever-evolving, AI-driven digital landscape, Deep Instinct can protect organizations and the integrity of their data.<h5>Introducing Deep Instinct Prevention for Storage</h5>Traditional storage solutions – whether in cloud environments or through network attached storage (NAS) vendors – focus on storage, not protection. While these solutions are successful at housing data, they lack the adequate protection necessary to protect in an increasingly sophisticated threat landscape. The legacy tools many organizations implement to supplement this lack of protection react to threats only once they’re in the environment, or interrupt workflows by sanitizing essential files into flat copies. And the tools that claim to be AI or machine learning (ML)-driven only detect <a href="https://info.deepinstinct.com/deep-learning-vs-machine-learning-in-cybersecurity" target="_blank">60 percent</a> of unknown attacks post-execution, allowing a zero-day threat or new malware variant time to execute and exhibit known bad behaviors before being caught. They also require human tuning and more time investment from security and IT teams. Organizations that take data protection seriously must leverage tools that actually protect their data. Deep learning (DL) is the solution.<a href="https://www.deepinstinct.com/prevention-for-storage" target="_self">Deep Instinct Prevention for Storage</a> (DPS) – part of our Deep Instinct Prevention Platform – applies a prevention-first approach to storage protection, stopping ransomware, known and unknown threats, and malware from reaching data and executing within the environment, wherever data is stored. Powered by Deep Instinct’s unique DL framework, DPS seamlessly integrates into NAS, hybrid, or public cloud environments while delivering unparalleled efficacy, accuracy, and enterprise-grade scalability. DPS provides proactive prevention that is scalable, saves time, offers ongoing security to comply with regulations, and quickly thwarts cyberthreats before they impact companies’ bottom line.With DPS, organizations can achieve the following:<ul><li>Improve SOC operations and lower TCO: DPS enables teams to reliably and quickly scan millions of documents per day, taking &lt;20 milliseconds per file – 750x faster than the fastest known ransomware can encrypt. It also helps teams confidently assess potential threats with a false positive rate of &lt;0.1% and &gt;99% efficacy in preventing unknown threats.</li><li>Manage solutions and maintain compliance: DPS natively integrates with storage infrastructure, including Dell CAVA and NetApp Vscan, and integration with SIEM and SOAR.</li><li>Streamline threat analysis: DPS automatically logs all file scans and generates detailed events for each malicious file, which are sent directly to the management console. Teams can control and manage remediation actions within storage, including quarantine or deletion of malicious files, as well as restore from quarantine, if needed. It also offers security status at-a-glance with single console management across all Deep Instinct products.</li><li>Ensure data privacy: Data never leaves an organization’s environment. They stay compliant with ensured data privacy and logs from every file scanned.</li></ul>As attacks increase in volume and velocity and emerging technologies like AI expand the threat landscape, a reactive or incomplete approach to data security is no longer sufficient. Organizations must continually scan data to account for the most advanced phishing, malware, and ransomware attacks. Only DL can offer that.You can learn more about this innovative new solution in the following ways:<ul><li>Read the new eBook, <a href="https://info.deepinstinct.com/ai-driven-cyberstorage" target="_blank">AI-driven Cyberstorage: Protecting Organizations’ Most Valuable Asset</a></li><li>Watch our webinar from Wednesday, November 1st, below:</li></ul>

DSX for Cloud - Amazon S3 provides natively integrated preemptive data security for Amazon S3.

Yariv Fishman

From Yariv Fishman.