Discover how AI is transforming cybersecurity with faster threat detection, automated responses, and smarter analytics. Learn about AI security challenges, data privacy, ethical governance, and industry applications in finance, healthcare, government, and manufacturing, plus best practices for building resilient AI-driven defenses.
Introduction
AI has become a transformative force for businesses across industries. Since the release of ChatGPT in December 2022, we’ve seen the rapid rise of generative AI, marking a key moment in the evolution of artificial intelligence. Many organisations view AI as a way to gain a competitive edge, with growing interest across areas such as Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Robotics, and now gen AI.
Business leaders increasingly recognise AI’s potential to reshape their industries. As confidence in its benefits grows, generative AI and AI/ML services are becoming central to technology strategies.
Artificial Intelligence (AI): Traditional AI vs. Generative AI
Artificial Intelligence services includes traditional techniques like Predictive AI and Causal AI. Predictive AI (or Analytical AI) uses algorithms to forecast future events based on historical data. Causal AI determines the causes and effects of events and is used in IT operations and AIOps.
Generative AI (Gen AI) services, leveraging foundational models such as large language models (LLMs), creates new content—text, code, images, audio, and more—by learning patterns from large datasets. This technology builds on existing data to generate novel outputs.
Gen AI use cases – 6C Framework
Gen AI might not be suitable for all use cases. It must be used complementary to Traditional or Discriminative AI. Our Gen AI use cases – 6C Framework – gives a broader direction for identifying use cases for Gen AI. According to our 6C Framework, its use cases fall under six broader categories.
Predictive AI
- Classification
- Regression
- Clustering
- Forecasting, Prediction
Causal AI
- Root cause analysis
- Anomaly detection
- Event corelation
Emergence of Hyper-modal AI (Gen AI + Predictive AI + Causal AI)
Hyper-modal AI combines Generative AI, Predictive AI, and Causal AI to address complex use cases. For example, in drug discovery, Predictive AI identifies potential drugs, while Causal AI analyses gene interactions to find the most effective treatment.
IT observability and operations tool maker Dynatrace recently announced their Hyper-model AI tool Davis. Historically, Davis has utilised Causal AI and Predictive AI for many AIOps use cases. Now they have augmented Davis with Generative AI capabilities, including the capability of interacting with the tool with natural language, Copilot AI Agent, and generating code for automation workflows.
Generative AI: Consume vs Customise vs Code Decision Framework
Gen AI Architecture Stack
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- Specialised infrastructure: Building and training Artificial Intelligence and Gen AI Models require specialised hardware and infrastructure, including GPUs, FPGA, fast and efficient networking (InfiniBand, Hollow core fibre/ HCF, RoCE / RDMA over Converged Ethernet, RDMA/ Remote Direct Memory Access), modern storage solutions, including cloud-based storage, and overall next-generation data centre technologies.
- Data and cloud: Success of your AI programmes would depend on your data strategy. Build a strong data foundation. Consider data as a product. Utilise modern data architecture patterns. Think about data lineage, data quality and data governance. The specialised infrastructure for AI can be easily accessed through a cloud service provider. Apart from infrastructure cloud service, providers also provide other services, including machine learning as a service, foundational model/ LLM as a service, data services, AI security, governance, RAI, etc.
- Foundational models: Foundational models power Gen AI. Models could be categorised by modality. Single modality models can generate only a single output type, e.g. either text or image. Multi-modality models can generate content multiple output type such as code, text, image, and video. Models also could be categorised by the number of parameters – Large language models (LLM) and small language models (SLM). Models also could be open source or closed source.
- Model customisation: Models need to be customised so that the model can understand your organisation, your client and your sector. This could be done in a number of ways, including prompt engineering, RAG (retrieval augmented generation), fine tuning and pre-training.
- Orchestrator: Our orchestration solutions integrate various AI capabilities to streamline operations and enhance efficiency.
- Gen AI apps: Gen AI powered apps include existing apps where new Gen AI powered features are added, such as newly built intelligent apps, AI agents and chatbots, digital assistants, etc.
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Generative AI and AI/ML services
Grant Thornton Bharat provides a comprehensive suite of GenAI and machine learning services designed to support organisations at every phase of their AI adoption journey. Whether you are in the early stages of exploring these technologies or working towards advanced integration, our experts deliver strategic insights and solutions to help you leverage AI for enhanced business outcomes.
Whitepaper
Rise of AI infrastructure
Having a well thought through architecture stack is important for adopting and scaling AI and Gen AI programs across your organization. The AI architecture stack comprises of specialised infrastructure for Gen AI, cloud and data services, model as a service layer (provides a catalogue of foundational models from different providers, and model customisation), middleware (model orchestration tools and APIs), and AI powered Intelligent App. Read our whitepaper to know more about the AI architecture stack.
AI ethics, security and governance – Responsible AI (RAI) and Explainable AI (XAI)
As AI systems and agents are being used in critical and impactful use cases from identifying the right candidate for a job to underwriting insurance to different healthcare and life sciences, use cases ensuring that these systems are unbiased, fair, safe, secure and reliable should be the topmost priority. These systems should not violate intellectual property (IP) and should abide by AI and data-related laws, regulations and compliances (e.g. the newly formed EU AI Act, Artificial Intelligence and Data Act (AIDA by Govt of Canada), GDPR, etc). Overall, AI systems should protect and reinforce positive human values.
Our responsible Artificial Intelligence services framework covers all aspects of AI ethics and governance. We utilise this framework in all our Gen AI and AI/ML projects.
1.
Fairness and equality
2.
Inclusivity & non-discrimination
3.
Reliability and safety
4.
Security and privacy
5.
Intellectual property
6.
Regulations & compliance
7.
Transparency, explainability and interpretability
8.
Protection and reinforcement of positive human values
9.
Accountability
10.
Governance
Scale your AI Initiatives – Our AI adoption framework
We can help you end-to-end, starting from AI readiness assessment to identifying and prioritising the optimum use cases for you to Gen AI POC to design and build. Our AI ethics and governance services include all aspects of Responsible AI, FinOps for AI workloads and Sustainable AI.
Our Gen AI and AI/ML services
We can help you end-to-end, starting from AI readiness assessment to identifying and prioritising the optimum use cases for you to Gen AI POC to design and build. Our AI ethics and governance services include all aspects of Responsible AI, FinOps for AI workloads and Sustainable AI.
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