Executive Programme in Artificial Intelligence and Cyber Security for Organizations [EPAI&CSO-04]

Executive Programme in Artificial Intelligence and Cyber Security for Organizations (EPAI&CSO-04)

Associate for Technical and Student Support: Jaro Education

Programme Overview

The Executive Programme in Artificial Intelligence & Cyber Security for Organizations Programme Offered by IIM Indore is a comprehensive, industry-oriented programme designed for non-technical professionals, engineers, IT project managers, functional managers, and early-career IT professionals seeking to build future-ready capabilities in AI and cybersecurity.

As organizations rapidly adopt AI-driven technologies, cyber risk has become a business challenge, not just an IT concern. This multidisciplinary programme equips participants with the knowledge to understand digital transformation, leverage AI responsibly, identify cyber threats, manage organizational risk, and contribute to secure business decision-making.

The programme is ideal for professionals looking to enhance cross-functional technology leadership, transition into cybersecurity roles, or strengthen their ability to navigate today’s evolving digital landscape.

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Programme Highlights

Executive Education Alumni Status- IIM Indore
Certificate of Completion from IIM Indore
Designed for working Executives
Holistic Curriculum
Interactive Online Learning
Curriculum taught by Best of the Domain Experts

Learning Outcomes

Understand cybersecurity threats, technologies, and best practices.
Apply risk management and security measures like firewalls and encryption.
Investigate cyber incidents and comply with regulations.
Communicate with stakeholders and lead security teams.
Gain hands-on experience and industry networking for career growth.
Learn key AI concepts, including machine learning and neural networks.
Apply AI algorithms to real-world problems.
Develop skills in Python, big data, and model optimization.
Explore ethical AI considerations and emerging trends.
Engage in hands-on projects and interdisciplinary teamwork.

Programme Objectives

Artificial Intelligence

The programme aims to:

Build a comprehensive understanding of Artificial Intelligence, Machine Learning, Generative AI, and data-driven decision-making.
Equip participants with practical technical skills in AI development, programming, analytics, and model deployment.
Enable professionals to apply AI solutions across diverse business functions and industries.
Develop the ability to evaluate AI models based on performance, scalability, reliability, and business impact.
Foster an understanding of responsible AI, including ethics, governance, privacy, and regulatory considerations.
Integrate concepts from computer science, data analytics, mathematics, and business strategy to create a holistic understanding of AI.
Encourage innovation by exposing participants to emerging technologies such as Large Language Models (LLMs), Blockchain, and Quantum Computing.
Prepare participants to lead AI-enabled digital transformation initiatives within their organizations.

Cyber Security

The programme aims to:

Develop a comprehensive understanding of the modern cybersecurity landscape, including evolving threats, vulnerabilities, and defense strategies.
Equip participants with practical knowledge of cyber risk management, security technologies, governance, compliance, and incident response.
Enable participants to identify, assess, and mitigate cyber risks across enterprise environments.
Build the capability to apply cybersecurity concepts through real-world case studies and hands-on learning.
Strengthen participants’ ability to communicate cyber risks effectively to business leaders and key stakeholders.
Foster a multidisciplinary approach that combines technology, governance, risk management, and organizational resilience.
Provide exposure to industry best practices, enabling participants to make informed security decisions in AI-driven organizations.
Prepare professionals for career growth in cybersecurity, digital risk, governance, and technology leadership roles.

Learning Outcomes- Artificial Intelligence

By the end of the programme, participants will be able to:

Build a strong foundation in AI concepts, including Machine Learning, Neural Networks, Natural Language Processing (NLP), Computer Vision, and Generative AI.
Apply AI and Machine Learning techniques to solve real-world business and organizational challenges.
Develop practical skills in Python and Big Data technologies such as Hadoop, Spark, and PySpark.
Prepare, analyze, and visualize data using industry-standard tools, including Power BI and Tableau.
Build, evaluate, and optimize AI models using best practices in data preprocessing, feature engineering, model validation, and performance tuning.
Understand the responsible use of AI by addressing ethical considerations such as bias, fairness, privacy, governance, and regulatory compliance.
Evaluate emerging AI technologies and identify opportunities to drive innovation and digital transformation within organizations.
Develop critical thinking and problem-solving skills through practical case studies and hands-on learning.

Learning Outcomes – Cyber Security

Participants will be able to:

Develop a comprehensive understanding of the evolving cybersecurity landscape, including emerging threats, technologies, and industry best practices.
Apply enterprise risk management frameworks to identify, assess, and mitigate cybersecurity risks across organizational environments.
Evaluate and implement cybersecurity technologies, including network security, intrusion detection, encryption, and endpoint protection solutions.
Lead cyber incident response and digital forensics investigations, including evidence collection, analysis, and recovery planning.
Interpret cybersecurity laws, regulations, and compliance frameworks to strengthen organizational governance and resilience.
Communicate cybersecurity strategy effectively with business stakeholders and lead cross-functional teams to foster a security-first culture.

Programme Contents

Part A: Artificial Intelligence for Organizations

MODULE A1 — Introduction to Artificial Intelligence & Digital Transformation

What is AI? History, evolution, and the current state of the field
Types of AI: Narrow AI, General AI, and the emerging AI Agent paradigm
Digital transformation frameworks and AI’s role in organizational change
AI adoption maturity models for enterprises
IAI in Indian and global industry contexts

Industry Case Study: How companies leverage AI-driven automation to transform service delivery models — and what it means for enterprise clients.

MODULE A2 — Machine Learning and AI

Supervised, unsupervised, and reinforcement learning fundamentals
Key algorithms: Decision Trees, Random Forests, SVMs, Neural Networks
Model training, validation, and evaluation frameworks
Feature engineering and data preprocessing pipelines
Practical ML with Python (scikit-learn, Pandas, NumPy)
Introduction to deep learning and computer vision
Natural Language Processing (NLP) fundamentals

Learning Outcomes:

Build and evaluate basic ML models using Python
Understand the lifecycle of an AI/ML project
Critically assess AI model outputs and their business implications

Tools Exposure: Python, Jupyter Notebooks, Google Colab, scikit-learn

Industry Case Study: How Bank uses ML models for credit scoring and fraud detection — including model governance challenges.

MODULE A3 — Big Data and Analytics

The 4Vs of Big Data: Volume, Velocity, Variety, Veracity
Hadoop ecosystem: HDFS, MapReduce, Hive, HBase
Apache Spark and PySpark for large-scale data processing
Data lakes vs. data warehouses: architectural decisions
Real-time streaming analytics (using PySpark)
Data-driven decision making for managers

Learning Outcomes:

Understand how organizations store, process, and derive insight from large datasets
Use PySpark for basic data transformations and analysis
Evaluate big data infrastructure options for organizational contexts

Tools Exposure: Apache Spark, PySpark, Hadoop (conceptual), Hive

Industry Case Study: How food aggregators use real-time big data pipelines to optimize delivery and demand forecasting.

MODULE A4 — Data Visualization

Principles of effective data visualization for executive audiences
Dashboard design: what to show vs. what to hide
Hands-on with Power BI: building dashboards and reports
Hands-on with Tableau: storytelling with data
KPIs and metrics design for cybersecurity and AI operations

Learning Outcomes:

Build interactive dashboards using Power BI or Tableau
Design visualizations that drive business decisions
Communicate data insights to non-technical stakeholders

Tools Exposure: Power BI, Tableau

Industry Case Study: How a leading BFSI firm uses Power BI dashboards for real-time cybersecurity risk monitoring at the board level.

MODULE A5 — Generative AI including Prompt Engineering (Enhanced)

How generative AI works: LLMs, diffusion models, transformers
Key platforms: ChatGPT, Claude, Gemini, Copilot — capabilities and limitations
Prompt engineering techniques: zero-shot, few-shot, chain-of-thought
Enterprise use cases: content generation, code assistance, document automation
Risks of generative AI: hallucinations, data leakage, IP concerns
Integrating GenAI into organizational workflows responsibly
GenAI for cybersecurity: threat report generation, policy drafting, alert summarization

Learning Outcomes:

Design effective prompts for business and technical use cases
Evaluate GenAI tools for enterprise readiness and risk
Identify security and compliance risks in GenAI deployment

Tools Exposure: ChatGPT (API), Claude, Microsoft Copilot, Google Gemini

Industry Case Study: How a global law firm piloted GenAI for contract review — and the governance framework they built to manage risk.

MODULE A6 — Agentic AI and Autonomous Systems (New Module)

Why this matters: Agentic AI is the fastest-growing area of enterprise AI adoption in 2025-26. Organizations are deploying autonomous AI systems without adequate governance — creating both opportunity and serious risk. Professionals who understand this space will be indispensable.

What is Agentic AI? From chatbots to autonomous AI agents, difference between AI agents and Agentic AI
Architecture of AI agents: perception, reasoning, memory, action
Multi-agent systems and orchestration frameworks
Agentic AI in enterprise: automated workflows, decision pipelines, AI assistants
Security risks of agentic systems: prompt injection, agent manipulation, unintended actions
Governance and human-in-the-loop design for autonomous AI
Real-world deployments: AI agents in customer service, IT operations, finance

Learning Outcomes:

Understand how autonomous AI agents are architected and deployed
Evaluate organizational readiness and risk for agentic AI adoption
Design governance guardrails for AI agent deployment in their organization

Tools Exposure: Microsoft Copilot Studio, Zapier, n8n

Industry Case Study: How a global bank is deploying AI agents for reconciliation and compliance checks — and the oversight framework that keeps humans in control.

MODULE A7 — Emerging Technologies: Blockchain & Quantum Computing

Blockchain fundamentals: distributed ledgers, consensus mechanisms, smart contracts
Enterprise blockchain use cases: supply chain, identity, finance
Quantum computing basics: qubits, superposition, entanglement
Quantum’s threat to current encryption (post-quantum cryptography)
NIST post-quantum cryptographic standards overview
Strategic implications for organizational IT roadmaps

Learning Outcomes:

Understand how blockchain and quantum computing will reshape security and data management
Assess organizational exposure to quantum-era cryptographic vulnerabilities
Evaluate blockchain applicability to specific business contexts

Industry Case Study: India’s Central Bank Digital Currency (CBDC) pilot — blockchain design choices and security considerations.

PART B: Cyber Security for Organizations

MODULE B1 — Introduction to Cybersecurity: Issues and Challenges

The current global cybersecurity threat landscape and sector-specific cyber threat landscape
Foundational understanding of cybersecurity concepts
Types of threats: malware, ransomware, phishing, insider threats, APTs
AI enabled tech challenges
Leadership role in cybersecurity implementation

Learning Outcomes:

Describe the global and sector-specific cyber threat landscape and AI-related risks
Understand core cybersecurity concepts to assess risks and recommend basic technical and organizational controls
Lead and justify cybersecurity policies and implementation plans to reduce risk and ensure accountability

Industry Case Study: The AIIMS Delhi ransomware attack (2022)

MODULE B2 — Defensive and Offensive Cybersecurity Landscape

Understanding network fundamentals and reference models
Secure design principles and models
Offensive and defensive security
Penetration testing methodology and scope
Vulnerability scanning and assessment fundamentals
Social engineering and human-factor attacks

Learning Outcomes:

Explain network fundamentals, reference models, and secure design principles to evaluate system architectures for security
Understand offensive techniques and recommend defensive measures—detection, prevention, containment—and mitigate human-factor risks including social engineering
Penetration testing and vulnerability assessment fundamentals and methodologies

MODULE B3 — Security in the Interconnected World: Cloud, Mobile & IoT

Cloud security fundamentals and modern architecture
IoT security risks: device management, firmware vulnerabilities, network segmentation
Sector specific IoT and automation
IoT strategic insights and business challenges for an organization

Learning Outcomes:

Explain cloud security fundamentals and architectures
Identify IoT security risks and sector-specific automation challenges
Assess strategic and business implications of IoT adoption and apply case-study insights to recommend governance, business cost of failures and risk mitigation

Industry Case Study: Samsung: The Internet of Things

MODULE B4 — Governance, Risk Management & Compliance [ENHANCED]

Risk Management:

Cybersecurity risk management frameworks: NIST CSF, ISO 27001, COBIT
Risk registers, risk appetite, and risk treatment strategies

Compliance & Regulations:

India: IT Act 2000, DPDP Act 2023, CERT-In Directions, RBI Cyber Security Framework
Global: GDPR, HIPAA, SOC 2, PCI-DSS
EU AI Act: implications for organizations using or developing AI
Compliance audit processes and documentation requirements
Building a compliance calendar and reporting cadence

Governance:

Cybersecurity governance structures: CISO role, Security Committees, Board reporting
Developing an Information Security Management System (ISMS)
Security policy lifecycle: creation, approval, enforcement, review
Security metrics and KPIs for board-level reporting
AI governance frameworks: responsible AI policies, model risk management

Learning Outcomes:

Build and present a risk register for a real organizational scenario
Map organizational practices against NIST CSF or ISO 27001 controls
Draft a compliance roadmap for DPDP Act and CERT-In requirements
Understand board-level cybersecurity governance responsibilities

Industry Case Study:

GDPR vs. DPDP Act: A side-by-side comparison and what Indian organizations operating globally must address.

Why this depth matters: This programme targets managers and technology leaders who are accountable for organizational risk. Superficial compliance knowledge is insufficient — professionals need to operationalize governance frameworks, present to boards, and make risk-informed decisions.

MODULE B5 — Cybersecurity Technologies (Tool-Enhanced)

SIEM platforms: architecture, log management, correlation rules

Learning Outcomes:

Design a layered cybersecurity technology stack for an organization

Industry Case Study: How a global manufacturing company deployed CrowdStrike to detect and contain a nation-state intrusion within 4 hours — compared to their previous 3-week mean time to detect.

MODULE B6 — Incident Response and Digital Forensics

Business Continuity and Disaster Recovery Planning
Risk Assessment and Management
The incident response lifecycle: Preparation, Detection, Containment, Eradication, Recovery
Digital forensics fundamentals: chain of custody, digital evidence, investigation and evidence preservation
Tools and Techniques in Forensic Analysis (Conceptual) Post-incident analysis
Crisis communication during a cyber incident

Learning Outcomes:

Understand the incident response lifecycle for an organisation
Identify and preserve digital evidence to meet legal and evidentiary standards
Understand risk assessments and post-incident analysis, leveraging digital forensics fundamentals and conceptual forensic tools/techniques
Coordinate crisis communication and strengthen organisational resilience

Tools Exposure: Nmap, Wireshark, Metasploit (conceptual)

MODULE B7 — Ethical Hacking

Ethical hacking methodology and legal boundaries
Reconnaissance techniques and ethical considerations
Scanning and enumeration
Ethical hacking, compliance and best practices

Learning Outcomes:

Explore ethical hacking methodologies within legal and regulatory boundaries to assess system security
Understand reconnaissance, scanning, and enumeration techniques to identify vulnerabilities responsibly
Adhere to compliance requirements and best practices for reporting, remediation, and safe exploitation

MODULE B8 — Identity and Access Management (IAM)

Identity as the new security perimeter
Authentication mechanisms and challenges
Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC)
Zero Trust and IAM
Integration issues and connectivity
Identity governance and access reviews

Learning Outcomes:

Explain identity as the security perimeter and evaluate authentication mechanisms and common challenges
Understand access control and Zero Trust principles to manage access and integration across systems
Lead identity governance, conduct access reviews, and address connectivity and compliance issues

MODULE B9 — Data Privacy and Data Protection

Data privacy principles: purpose limitation, data minimization, consent
India’s Digital Personal Data Protection (DPDP) Act 2023: detailed walkthrough
GDPR essentials for organizations with global operations
HIPPA
Data classification frameworks and data inventory
Privacy by Design: embedding privacy into product and system development
Data breach notification requirements and timelines
AI and data privacy: handling personal data in ML models

Learning Outcomes:

Conduct a basic data inventory and classification exercise
Map organizational practices to DPDP Act compliance requirements
Draft a data breach response procedure aligned with regulatory timelines

Industry Case Study: The WhatsApp GDPR fine (2021) — what went wrong with privacy notices and how organizations can avoid similar penalties.

MODULE B10 — Overview of Cybercrime and Cyber Law

Typology of cybercrime and classification
Motivations and planning behind cyber attack
India’s IT Act 2000 and amendments and legal gap
IT Guidelines and rules
Digital Personal Data Protection Act, 2023
Cyber law and evidence admissibility – Indian Evidence Act
International regulation and enforcement challenges

Learning Outcomes:

Describe cybercrime typologies, attacker motivations, planning, and classifications
Explain key Indian cyber laws and regulations (IT Act 2000, its amendments, Digital Personal Data Protection Act 2023), evidence admissibility under the Indian Evidence Act, and identify legal gaps.
Assess international regulatory frameworks and enforcement challenges

Industry Case Study: Legal case of large-scale data breach and exposure of user records and privacy failures

MODULE B11 — Information Security: Strategy and Policy [ENHANCED]

Aligning cybersecurity strategy with organizational business objectives
Security budgeting: building a business case for security investments
Security program maturity models (CMMI for Security)
Developing and communicating a multi-year cybersecurity roadmap
Security culture and behaviour change programs
Vendor and procurement security assessment frameworks
M&A cybersecurity due diligence
Communicating cyber risk to boards and audit committees (using language executives understand)

Learning Outcomes:

Develop a 3-year cybersecurity strategic roadmap for a sample organization
Build a board-level cybersecurity risk briefing (non-technical language)
Evaluate security investment decisions using risk-adjusted ROI frameworks

Industry Case Study: How the CISO of a Fortune 500 company restructured the security budget from reactive to proactive — and the metrics they used to justify it to the CFO.

MODULE B12 — AI Security Operations (AI SecOps)

Securing the AI/ML lifecycle: data pipelines, model training, deployment
Adversarial machine learning: model poisoning, evasion attacks, model inversion
AI-powered threat detection: behavioral analytics, anomaly detection at scale
Securing LLMs and generative AI deployments (prompt injection, data leakage)
AI governance for security tools: bias in automated decision-making
Evaluating AI-powered security products critically

Learning Outcomes:

Identify security risks in AI system deployment within their organization
Evaluate AI-powered cybersecurity tools with appropriate critical judgment
Understand how attackers are using AI to enhance attacks

Industry Case Study: How attackers used AI-generated deepfake audio to impersonate a CEO and authorize a $25M wire transfer — and the detection controls that could have stopped it.

Disclaimer: The sequencing of modules and topics is subject to the availability of teaching faculty. The curriculum matrix is subject to change based on emerging industry trends and academic requirements.

Duration & Number of Session Hours

Session Duration: 10 months

Number of Hours: 87.5 (Approx.)

Number of Sessions (75 Minutes each): 70

Online Sessions: 58

On-Campus Module: 3 Days Duration (12 Sessions)

One or two sessions from some courses will become part of the on-campus orientation module. In case the on-campus module is not conducted due to the COVID situation, the same will be included in the total number of sessions.

*The programme duration may be slightly extended due to issues like faculty unavailability and gazetted holidays on the session days.

Eligibility

Diploma (10+2+3)/ Graduate / Post Graduate from Universities recognised by the Association of Indian Universities with minimum 50% marks in either Diploma or graduation or post-graduation (or its equivalent) with at least two years of work experience.
*Internships and Trainee experiences are not considered Full-time work experience

Attendance Criteria

Participants are expected to attend all sessions of a given course. However, Participants may take leave on account of emergencies, subject to the approval of the Programme Coordinator. However, 75% minimum attendance requirement would be considered for the final grading. For less than 75% attendance, a grade cut as per the norms will be applied.

Evaluation Methodology

The performance of participants will be monitored on a continuous evaluation basis through quizzes, assignments, tests and examinations. The participant is required to score minimum of marks/grades as decided by the Institute from time to time to complete the course.

Certification

Participants who successfully meet the evaluation criteria and satisfy the requisite attendance criteria will be awarded a 'Certification of Completion' from IIM Indore
Note: The sample certificate is indicative. The Institute reserves the right to revise it.

Programme Schedule

Courses will be delivered through online mode using an appropriate synchronized technology platform. Lectures will be delivered through broadband-based technology involving two-way audio and video communication. Sessions will be held once a week, generally on weekends. Participants can attend sessions directly from their desktop/laptop (Direct-to-Device). Participants will be provided with reading materials and other course resources for each course. They may also interact with the concerned faculty through email or chat.
- The academic sessions will start from December 2026. Sessions will be held every Saturday.
- The normal session timings (IST) will be as follows:
  - Session 1: 5:30 PM to 6:45 PM (IST)
  - Session 2: 7:00 PM to 8:15 PM (IST)

The time gap between two consecutive sessions is to give the participants a break. On some days, the classes may extend beyond the mentioned time. In addition to attending interactive sessions, participants have to undertake online quizzes, assignments, and examinations.

Who Should Enrol?

This programme is designed for professionals looking to build strategic capabilities in Artificial Intelligence and Cyber Security, regardless of their technical background.

Graduates with a minimum of 2 years of post-qualification work experience.
Non-engineers and non-technical professionals seeking to develop AI and cybersecurity literacy for business and leadership roles.
Managers and functional leaders responsible for technology, digital transformation, governance, risk, or business operations.
Technology leaders and IT managers looking to strengthen their understanding of cyber risk, AI adoption, and organizational resilience.
Engineers, IT professionals, and project managers seeking to upskill in AI, cybersecurity, and emerging technologies.
Cybersecurity aspirants looking to build expertise in threat management, cyber risk, governance, and security operations to accelerate their careers.
Career switchers from business, operations, finance, consulting, or other domains who wish to transition into AI, cybersecurity, or digital risk management.
Professionals involved in digital transformation initiatives who need to make informed decisions on AI adoption, cybersecurity, governance, and enterprise risk.

Note: The programme follows a multidisciplinary, business-focused approach and is equally valuable for technical and non-technical professionals aspiring to lead AI-enabled, secure organizations

Programme Activity Timelines

Programme Schedule	Technical Orientation Date 18th December 2026
	Academic Orientation Date 19th December 2026
	Programme Commencement Date 26st December 2026
Application Closure Date	Closing Soon

Programme Fee and Payment Schedule

Fee Particulars	Amount
Registration Fee (while applying)	INR 2,500/-
Total Fee (exclusive of GST @18%)	INR 2,00,000/-
Overall Fees (including GST @18%)	INR 2,36,000/-
Instalment Pattern
1^st Instalment (due 10^th December 2026)	INR 70,000/-
2^nd Instalment (due 15^th Feb. 2027)	INR 70,000/-
3^rd Instalment (due 15^th May 2027)	INR 57,500/-

*The above fee does not include the Executive Education Alumni fee.

*GST will be charged extra on these components; at present it is @18%.

Students’ Privileges

Executive Education Alumni Status

Participants who successfully complete the programme are eligible to apply for Executive Education Alumni Status at IIM Indore. This is a separate, optional registration requiring a fee of INR 10,000 plus 18% GST per person (lifetime membership) to register their name with the IIM Indore Executive Education Alumni.

Benefits include:

Subscription to brochures and newsletters from IIM Indore
Access to the IIM Indore Learning Centre (on-campus access only)
An official email ID from the institute
Executive Education Alumni Status is granted upon request and subject to completion of the application process and verification by the Alumni Office.

Please note that successful completion of the programme and payment of the fee do not automatically guarantee alumni status. The Institute reserves the right to approve or decline applications for Executive Education Alumni Status.

IIM Indore also reserves the right to modify these terms at its discretion without prior notice. Any disputes arising shall fall under the exclusive jurisdiction of the courts at Indore.

For more details: Executive Alumni – IIM Indore

For more details contact: olpoffice@iimidr.ac.in

Tel.: 0731-2439733/739

You may also contact the Academic Counsellor at our technical partner, Jaro Institute of Technology Management & Research Ltd., at iimindore.admissions@jaro.in.

Alternatively, you may reach out to Jaro Education’s Programme Expert, Mr. Abhishek Singh, at +91 8072569044 or Singh.abhishek@jaro.in.

Apply Now: https://www.jaroeducation.com/executive-programme-in-artificial-intelligence-and-cyber-security-for-organizations-iim-indore

Please note that IIM Indore reserves the right to change the programme design, format, number of sessions, certificate format, terms in the programme or can incorporate any such change deemed necessary by the institute without prior intimation.