Skip links
CONTACT US

Top 10 DevSecOps Trends to Watch Out in 2024

Jump To Section

  • Nayan Naidu
  • 5 mins

The evolution of DevSecOps has been marked by a continuous drive for efficiency, security, and innovation. With the advent of GenAI and more advancements expected to keep coming, DevSecOps becomes an important aspect of how a business builds and delivers a product to the market with proactive security and ethics. Here, you can delve into detailed DevSecOps trends.

Enterprises are not just about making and selling something anymore. There is a lot more to an organization today as key influencers on our society and the world. Let’s look at how the convergence of technology, culture, and business is expected to shape the future of DevSecOps in 2024.

1. Autonomous Systems Integration and Self-Service Infrastructure

Leveraging AI-driven autonomous systems, DevOps workflows witness a seismic shift towards automation. Machine learning augments processes, enhancing efficiency and error mitigation across development, testing, and deployment pipelines. All these focused with Self-Service Infrastructure, enables the ease of Infra availability for the business users to solve their problems, quicker than earlier. 

2. Security as Code (SAC)

The paradigm shift towards embedding security practices within the development pipeline, termed Security as Code (SAC), empowers proactive cybersecurity measures. This integration fortifies software robustness from its inception without compromising speed.

3. GitOps and Progressive Delivery

The integration of GitOps methodologies and progressive delivery strategies streamlines deployment by utilizing version control systems. This fusion enables continuous delivery while ensuring control and traceability in complex software ecosystems.

4. Kubernetes and Container Orchestration

The undeniable ascendance of Kubernetes as a container orchestration platform underscores its pivotal role in facilitating scalable, resilient, and portable containerized applications within DevOps workflows.

5. Cloud-Native Development

The shift towards cloud-native development reshapes infrastructure dynamics, harnessing microservices and serverless architectures. This transition fosters agility, scalability, and cost-effectiveness, demanding robust DevSecOps integration.

6. Observability and AIOps

The convergence of observability and AI-driven operations introduces a new era in system monitoring and issue resolution. AIOps augments human capabilities, enabling predictive analysis and real-time anomaly resolution.

7. DevSecOps Culture and Collaboration

As it is famously said, “Culture eats strategy for breakfast”. Cultural transformation remains imperative within organizations. Fostering collaboration and instilling a DevSecOps mindset across teams is fundamental for successful implementation and innovation. 

8. Compliance and Regulatory Integration

Incorporating compliance and regulatory requirements into DevSecOps workflows is now a necessity. Automation streamlines compliance checks, ensuring adherence without impeding development velocity. Compliance requirements and policies, are not gets embedded as part of the intelligent pipelines, that help in achieving these requirements. 

9. Infrastructure as Code (IaC)

The evolution of Infrastructure as Code (IaC) redefines infrastructure provisioning and management. Codifying infrastructure configuration enhances consistency and expedites recovery in case of failures.

10. Quantum Computing and DevOps

The impending impact of quantum computing on DevOps methodologies presents both challenges and unprecedented computational capabilities. Integrating quantum computing into development workflows awaits exploration.

The Way Forward: Bridging Platform Engineering and DevOps with Collaborative Convergence, addressing Data Engineering and Analytics Requirements

The convergence of platform engineering and DevOps signifies a shift from distinct entities to symbiotic collaboration. While DevOps focuses on methodologies for software delivery, platform engineering emphasizes building the foundation and tools enabling these practices. Platform which is secure, scalable and dependable, is the need of the hour. 

Collaborative Synergy: Uniting DevOps and Platform Engineering

The crux of DevOps and platform engineering collaboration hinges on seamless communication and integration. Aligning workflows, enhancing automation, and fortifying security measures are imperative outcomes of this synergy. With self-healing and self-managed infra, having security built in, in the next wave that we would be flowing on. 

Balancing Innovation and Practicality: The Hype Cycle

Amidst the DevOps trends’ hype cycle, discernment between innovation and practical application becomes pivotal. Striking a balance between experimentation and pragmatism ensures alignment with organizational goals and augments DevSecOps strategies. Adopting the continuous learning cycle, as part of the strategy, is definitely the way to go. 

Nayan Naidu

Nayan Naidu

Latest Reads

Subscribe

Suggested Reading

Empowering Retail Excellence with AI-Powered Transformation

Empowering Retail Excellence with AI-Powered Transformation

AnalyticsPlatform Modernization
Streamlining Data Delivery and Quality Optimization in Enterprise Systems

Streamlining Data Delivery and Quality Optimization in Enterprise Systems

Data EngineeringDigital Business
AI Augments and Human Intelligence: An Evolution

AI Augments and Human Intelligence: An Evolution

Data EngineeringDigital Business

Ready to Unlock Yours Enterprise's Full Potential?

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes