ILLUME's AI Driven Cyber Attack Mitigation Plan for India

ILLUME Intelligence's AI-Driven Cyber Attack Mitigation Plan for India

AI-driven attacks use machine learning, automation, deepfakes, adversarial models, and AI-powered malware to exploit vulnerabilities faster and at scale.

Our mitigation plan ensures:

Early detection of AI-enabled threats

Protection of AI models and datasets

Compliance with Indian regulatory frameworks

Business continuity and cyber resilience

Aligned with:

Information Technology Act, 2000

Digital Personal Data Protection Act, 2023

CERT-In Guidelines

NCIIPC (if applicable)

AI-Driven Threat Landscape

ILLUME Intelligence faces risks such as:

External Threats

AI-generated phishing campaigns

Deepfake impersonation of executives

Automated vulnerability scanning bots

AI-powered ransomware

Adversarial attacks on deployed ML models

Internal Threats

Model poisoning

Data leakage from training datasets

Insider misuse of AI tools

Unauthorized model extraction

Strategic Mitigation Framework

A. Governance & Policy Controls

Establish AI Security Governance Committee
Define AI Risk Classification Framework
Mandatory AI system registration & audit
Incident reporting within 6 hours (as per CERT-In norms)
Vendor AI security compliance verification

B. Technical Controls

AI Threat Detection Systems

Deploy AI-based anomaly detection

Behavioral analytics for user & entity behavior (UEBA)

Automated threat intelligence feeds

AI Model Protection

Model watermarking

Differential privacy in training

Secure model hosting (HSM, encryption at rest & transit)

Adversarial robustness testing

Deepfake & Phishing Defense

Multi-factor authentication (MFA)

Voice/video deepfake detection tools

DMARC, SPF, DKIM email controls

Zero Trust Architecture

C. Data Protection Controls

Aligned with DPDP Act:

Data minimization policy

Encryption (AES-256, TLS 1.3)

Secure data pipelines

Role-based access control (RBAC)

Regular data audits
D. Incident Response Plan (AI-Specific)
Phase 1 – Detection

Automated AI anomaly alerts

SOC 24/7 monitoring

Phase 2 – Containment

Isolate affected models or systems

Disable compromised credentials

Phase 3 – Investigation

Model integrity verification

Log analysis

Forensic review

Phase 4 – Reporting

Notify CERT-In (if reportable incident)

Regulatory compliance notifications

Phase 5 – Recovery

Restore clean model backups

Patch vulnerabilities

Revalidate AI system performance

Workforce Readiness

AI security awareness training

Phishing simulation exercises

Secure AI development lifecycle (Secure MLOps)

Red Team vs Blue Team AI simulations

Business Continuity & Resilience

AI system redundancy

Secure cloud backups

Disaster recovery drills

Crisis communication plan

Continuous Monitoring & Audit

Quarterly AI security audits

Annual third-party penetration testing

Bias and integrity testing of AI systems

Compliance review against Indian cyber regulations

Implementation Roadmap
Timeline    Action
0–3 Months    Risk assessment & governance setup
3–6 Months    Deploy AI detection + Zero Trust
6–9 Months    Model security hardening
9–12 Months    Full compliance audit & stress testing
Key Risk Indicators (KRIs)

Increase in abnormal login behavior

Model accuracy drift

Spike in automated traffic

Unauthorized API access attempts

Data exfiltration alerts

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