Humans: The Linchpin in a Decentralized, Security-Centric Approach for the Distributed Computing World

ByteSafe’s Raghavan Chellappan offers commentary on how humans are the linchpin in a decentralized, security-centric approach for distributed computing. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

In a world of hyper-scaled connected systems, the Internet of Things (IoT) and bring-your-own-device (BYOD) culture, systems are interconnected and devices are enabled by default to collect and share data over the internet or other communication networks, and the collected data is stored across distributed systems. In such an environment, a person’s digital identity serves as the key connective element in all digital interactions and transactions.

Distributed computing refers to the techniques and processes used to encrypt, process, and securely store (in a distributed manner) all the digital data that may be under a person’s control (either at rest or in motion) to ensure confidentiality, integrity, and availability across the data chain.

Additionally, as enterprises increasingly use quantum machines, generative AI (GenAI), and large language model (LLM) driven agents—all of which require heavy processing power—there is an increased demand for graphics processing unit (GPU) powered computing and distributed computing to manage the large volumes of data that are being processed through data-intensive applications.

The Problem and Why It Matters

Distributed computing faces several challenges, however, such as high inference latency, output uncertainty, inadequate evaluation metrics, and security vulnerabilities, which can delay response times, create inaccurate or invalid results, or even lead to data breaches. A data breach for example is costly and would tarnish the reputation of an organization.

When adopting emerging technologies like quantum computing and advanced neural network deep learning techniques (artificial, convolutional, or recurrent), security and privacy are at risk when deploying applications across distributed computing environments due to their susceptibility to attacks and data leakage. By applying social engineering techniques cybercriminals and data brokers are able to target and exploit human emotions and cognitive biases, leading to potential vulnerabilities and risks requiring mitigation.

Yet other issues arise when AI solutions are deployed in distributed environments because bad actors can use model inversion attacks to trick the AI model or prompt injections, thereby manipulating inputs and bypassing safety mechanisms, and leading the agent to generate harmful or unethical content. Such vulnerabilities can be exploited to spread misinformation, generate malicious code, or perform unauthorized actions.

In many instances, Agentic AI algorithmic decision-making relies on data tied to Personally Identifiable Information (PII) and Sensitive Personal Information (SPI). When such information is distributed across multiple systems, there is less human control over the data, and limited understanding of who has access, why data are collected and how data are being used. This in turn results in reduced trust in the system.

Lastly, most organizations often deploy a mix of legacy and cloud-native applications, making it challenging to identify, monitor and manage vulnerabilities and risks that span the enterprise.

So, the question becomes how best to address these concerns? As technologies evolve, security measures need to continue to function effectively, accommodating new systems and applications.

Redefining Security Approaches in Distributed Computing for Quantum and AI

Implementing robust security protocols is essential to protect user data and maintain trust across distributed systems. To achieve this, we need to redefine security architectures that leverage modern practices and methods, like MLOps, GenAI/LLMOps, AgentOps, and DataOps, to continually monitor, measure and protect data across various applications and platforms. Such an approach should also support a scalable, sovereign system of verifiable controls—including technical, legal and operational factors—while remaining technology agnostic. Ultimately this would improve data protection and management in cloud infrastructure and distributed systems.

The explosion of quantum-based solutions, artificial general intelligence (AGI), AI agents and agentic AI architectures has concomitantly resulted in myriad security- and privacy-related regulatory standards, as well as governance and compliance requirements for data collection, storage and processing across geographies that are overwhelming organizations.  Adhering to these benchmarks and regulations requires a culture of transparency, accountability, continuous monitoring and improvement with a human presence infused into the decision-making process (through oversight and intervention).

We need to future-proof privacy and security management architectures when implementing AI-driven distributed systems or services by centering humans in the process to manually validate sensitive outputs (human-in-the-loop approach) and apply a continuous improvement mindset.

As AI-driven applications continue to gain traction, organizations should align distributed computing systems with human values and ethical standards.

A Human-Centric Decentralized Security Controls Framework

Human-centricity is key in designing secure, distributed computing application systems that prioritize user needs while ensuring privacy, protection and efficiency. A human-centered approach benefits organizations that use multiple systems and applications, ensuring that sensitive personal information (SPI) remains secure regardless of where it is processed or stored.

To achieve these outcomes, we propose a simplified, decentralized, multi-layered, security control framework based on six (6) key components that include:

• Human-Presence Identity & Access Control
• Secure & Auditable Infrastructure
• Data Sovereignty & Integrity
• Embedded Privacy & Trust
• Governance, Risk & Compliance Trails
• Guardrails & Remedial Workflows

In this framework, security and privacy are treated as foundational design principles. The six (6) components work together to safeguard data regardless of the technology stack and systems used for distribution, allowing for scalability, flexibility, and interoperability in diverse IT environments. They also embed trust, telemetry, observability, and autonomous resilience into the fabric of distributed systems. This helps in addressing some of the challenges inherent in distributed systems such as slow response times, poor performance, weak security postures, inaccurate assessments, low reliability of results, weaknesses or flaws in a system’s design, data leakage, low data integrity and trust, and lack of availability of information systems.

Building trust in a distributed computing environment is vital. Users must feel confident that their data residing within an organization’s systems is secure and that the data collection, storage, and processing policies adhere to regulatory requirements and are transparent and accountable.

When it comes to credentialing and processing of personal and organizational data (whether directed, automated or volunteered) it requires a human presence to provide oversight and intervention in order to implement corrective action when needed. Using human-centric, decentralized applications and data architectures to efficiently protect and manage information allows human inputs to protect and maintain the integrity of a distributed system’s resources, structure, access controls, and data transport mechanisms. Implementing a multi-layered defense mechanism further enhances security and privacy in distributed environments.

Furthermore, harmonizing security standards and protocols for distributed systems supports better risk management in the long run.

Conclusion

Humans are integral to securing distributed computing and implementing secure systems is a collective responsibility.

By adopting a human-centric, technology- or system-agnostic, security control framework, organizations can enhance their overall security and privacy posture to protect and safeguard data across diverse applications and platforms in distributed environments. Human insight can potentially play a valuable role in protecting distributed digital systems by guiding the development of integrated solutions that involve a combination of infrastructure optimization, efficient deployment strategies, robust evaluation frameworks, and multi-layered security protocols, to enhance scalability, reliability, and ethical compliance of the system.

As distributed computing evolves, addressing its inherent challenges offers greater security, trust, and reliability in real-world applications as they become more complex and autonomous.