Dell Project Fort Zero
Cybercrime costs rose 1,237% globally in six years. Cybercriminals’ techniques get more advanced as cybercrime grows in pace and damage. Cybercrime is so ubiquitous that people and organizations are desensitized to it. The advantage in cyberspace has always belonged to adversaries. It just takes a few minutes to plan an attack; it takes considerably longer to defend. As a result, businesses are forced to engage in an asymmetric struggle for ownership over their data. With the use of artificial intelligence (AI), the gap that has long existed between organizations and cybercriminals using traditional perimeter-based security measures is being closed. As more businesses integrate AI into their security systems, the playing field is levelling up.
By utilising all available data, AI enables organisations to make decisions almost instantly, enabling them to monitor, detect, analyse, and respond to cyber threats. AI can identify behavioural anomalies and improve threat detection and response times by utilising huge language models in conjunction with machine learning. Artificial Intelligence also lessens the attack surface for cybercriminals and helps an organisation better comprehend the security landscape.
The compelling narrative of an organization’s increasing need for AI in every aspect is only as strong as the data that was used to create it. The foundation of Zero Trust is data protection. Organizations may leverage all of AI’s advantages while maintaining data quality by implementing Zero Trust principles.
Project Fort Zero Dell
Project Fort Zero (PFZ) offers a complete greenfield solution that is ready for use, and it was introduced at Dell Technologies World 2023. Following the criteria in the U.S. Department of Defense’s (DoD) Zero Trust Reference Architecture (ZTRA), Zero Trust adoption is made easier and faster with Project Fort Zero. Project Fort Zero is engineered to offer enhanced Zero Trust capabilities in compliance with the U.S. DoD ZTRA, backed by a top-tier vendor ecosystem. The Project Fort Zero system integrates the U.S. DoD ZTRA’s 45 capabilities and 152 activities into an independent on-premises enterprise private cloud. The U.S. DoD will evaluate Project Fort Zero’s advanced-level compliance in summer 2024.
Cybercrime cost $11.5 trillion worldwide in 2023. As outdated cybersecurity systems and procedures grow more prevalent, cybercrimes affecting sensitive data will rise. By 2023, the FBI received 880,418 cybercrime reports costing $12.5 billion, up from 467,361 in 2019. Increasing cybercrime costs and numbers have led organizations to use AI to modernize cybersecurity and protect themselves. AI empowers organizations to monitor, analyze, and detect cyber risks in real-time, providing a predictive view with content, prediction, and expertise.
To improve the efficiency and value of their operations, businesses are turning to artificial intelligence (AI), machine learning (ML), and large language models (LLM). According to a Stanford University survey from 2023, 55% of organizations integrated AI into at least one business unit; this represents a 175% increase in just seven years, up from 20% in 2017. Strong data policies and procedures are needed to build the LLMs because of the complexity of AI and the infrastructure that supports it.
The end products are complex AI models that produce large volumes of data and capture important organizational knowledge. AI models are important pieces of intellectual property that might cause organizations great harm if they were misplaced, stolen, or tampered with. Cybercriminals are likely to target AI models because of their high value. AI-related data must be protected at all costs because it contains sensitive consumer information, financial data, and time.
Artificial intelligence offers a way for quickly analyzing complicated data, which is necessary in order to promptly respond to cybercrime. Large volumes of data are produced by the application of AI, including proprietary models and algorithms that might contain crucial organizational and consumer data. Artificial Intelligence (AI) simulates human intelligence in machines, whereas Machine Learning (ML) enables AI to function. The goal of machine learning is to employ AI models to improve and teach.
Algorithms for machine learning educate AI how to react and recognize patterns seen in machine learning data. An LLM is an AI method that processes and generalizes big datasets using deep learning techniques. LLMs can be used for problem solving and behavior prediction in the future. A company’s AI models, ML algorithms, business models, and customer trust would all suffer greatly if this data were lost.
Making decisions that take use of AI depends on high-quality data. It is necessary to filter, examine, and compile the collected data into machine learning algorithms. When employing AI, a surprising amount of data is needed; nevertheless, for the data to yield the greatest results, it must be free from unauthorized changes. AI models are valuable resources for training and knowledge, but the veracity of the data they rely on determines how reliable the insight is. Since data is used to construct AI models, data authenticity is vital. AI models are crucial because they give corporate knowledge operational capabilities and a focus on it. To guarantee the validity of AI and ML models, data security is essential.
Zero Trust represents data protection in today’s dynamic cyber world. With its data-centric methodology, Zero Trust protects private data on a variety of platforms, applications, and contexts. By guaranteeing that only authorised individuals have access to data, Zero Trust strengthens data protection. It is founded on the fundamental idea that assets or user accounts should never be implicitly trusted based just on their physical or network location. In order to implement Zero Trust principles, traditional perimeter-based network security solutions must give way to a microsegmented, data-centric design. An alternate multifactor authentication mechanism (MFA) must be supported by the Identity Provider (IdP).
Self-service is used to manage multifactor alternatives, which support biometric capability.
Protecting Data and AI Models
The data that AI models use determines how valuable they are. To fully utilise artificial intelligence, high-quality data is necessary. Inaccurate analysis, suggestions, and actions could be the outcome of flawed data. Complex data pipelines and computational power are needed for artificial intelligence to build LLMs. Large volumes of data are produced and used by these pipelines. The outcomes serve as the intellectual property that guides decisions and are crucial to organizations.
Because operations depend on the security of this data, it is even more important to guarantee the security of AI models and the data that supports them. Data on artificial intelligence is used to train AI models, improve revenue, and influence business decisions. It might be too much to lose these important records. The best way for organizations to protect AI assets and models is to develop and store them on a secure network that requires multiple identity verifications.
Cybercriminals can now easily go via numerous networks, once they get past the perimeter, into an organization’s system, giving them opportunity to alter and steal data. The Zero Trust Reference Architecture (ZTRA) developed by the U.S. Department of Defence (DoD) outlines five principles that form the basis for influencing Zero Trust:
Presume a hostile environment, assume a breach, never trust, always double-check, examine in detail, and use unified analytics.
By concentrating on the principles, Zero Trust can apply safeguards to preserve data, examine behavior anomalies, and take proactive steps to reduce lateral movement. In the event of a breach, an advanced Zero Trust solution lessens the impact on operations by limiting the capacity of cybercriminals to access, steal, corrupt, or modify data. Combining AI with Zero Trust allows for the near-real-time analysis of massive amounts of data, minimizing the need for human interaction, automating security measures, and decreasing the attack surface of an organization.
AI/ML is being used by Project Fort Zero to automate access policies, tag and classify data, avoid data loss, analyze behavior, and give conditional user access. It also helps with network analytics decision-making. In order to continuously enhance decision-making and teach AI/ML algorithms iteratively, Project Fort Zero uses extensive tracking of all activity. After being put into practice, AI/ML systems will use ongoing monitoring to automatically update security profiles. Through the identification, procurement, and implementation of policies, users can automate process operations by utilising current AI/ML functionality.
Using AI and self-learning capabilities, the Project Fort Zero system analyses attack patterns, automates data restoration, and fortifies defenses. These features shorten the time needed for extensive data analysis and enhance threat detection. Organizations can swiftly and correctly identify, evaluate, and respond to undesired activity by utilizing Project Fort Zero‘s AI/ML capabilities.
In conclusion
Zero Trust principles can be adopted more quickly thanks to the Project Fort Zero end-to-end solution. Businesses who use Project Fort Zero will be in the greatest position to ward off the most advanced cyberattacks. The necessity to protect data becomes even more important as the need for AI adoption rises dramatically. To protect their company from harm and maintain market stability, organizations need to secure important information such as language models, policies, consumer behavior, and key data. Information must still be protected from cybercriminals by safeguarding crucial organizational components.
The necessity of deploying AI and Zero Trust solutions is being driven by current trends and global influence. For organizations to effectively battle the rise in cybercrime, they need to modernise their cybersecurity posture and change their perspective. AI is the way of the future, and companies using AI can realise their full potential with an advanced Zero Trust solution.
