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Tackling the 12 National Security AI Challenges: A Deep Dive into NCIIPC's Grand Challenge



Introduction

The NCIIPC Startup India AI Grand Challenge invites bold AI innovators—startups and student teams—to address 12 critical problem statements pivotal for enhancing India's national security ecosystem. Each problem spans domains such as cybersecurity, geospatial intelligence, maritime surveillance, and more. In this blog, we’ll explore each challenge thoroughly, outlining problem context, technology pathways, and practical solution ideas. 

Problem Statement 1: LLMs to Detect Vulnerability in Open-Source Software

Context: Secure coding in open-source systems is essential. This challenge seeks AI tools that can detect software vulnerabilities (like buffer overflows or SQL injections) and suggest mitigations. 

Possible Approaches:

  • Curated CVE Datasets: Build training corpora from open-source repositories annotated with known CVEs (CVE databases, CodeQL, etc.).

  • Fine-Tuned Code Models: Adapt code-focused LLMs (e.g., CodeLlama, StarCoder) to highlight vulnerability patterns and offer patches.

  • Hybrid Pipelines: Combine static analysis tools (e.g., semgrep) with LLMs to detect issues and auto-generate fixes.

  • Explainability: Provide rationale via annotated code and natural-language explanations to facilitate developer trust.

  • Pipeline Flow:

    1. Input repository or file.

    2. Pre-screen by static analyzer.

    3. LLM generates vulnerability reasoning and remediation code.

    4. Diff-like interface for patch suggestions before integration.

  • Deployment Tips: Integrate into CI/CD pipelines; offer REST APIs or IDE plugins for continuous developer feedback.

Problem Statement 2: Phishing Detection for Specific Organizations

Context: Phishing attacks often target organizations with mimicked branding and style. This challenge aims to detect organization-specific phishing domains and pages. 

Possible Approaches:

  • Synthetic Phishing Generation: Use internal email/text samples to train a paraphrase model generating realistic phishing samples for training.

  • Classifier + Features: Extract visual cues (logos, design templates) and domain metadata (SSL mismatches, WHOIS anomalies) for ML models.

  • Graph-Based Models: Build graphs of legitimate domains, email headers, and certificate chains; detect anomalies via graph embeddings or GNNs.

  • Human-in-the-Loop: For flagged content, provide a quick dashboard for analysts to review and validate.

  • Deployment Tips: Real-time email scanning or browser plugin; scalable via feedback-driven continual improvements.

Problem Statement 3: Visual Search, Retrieval & Detection in Satellite Imagery

Context: Automatically finding objects across satellite imagery and generating labeled datasets is critical for intelligence tasks. 

Possible Approaches:

  • Two-Stage Object Detection: Use light, fast models for initial detection (e.g., YOLO), followed by high-precision models (e.g., Swin Transformers) for refined output.

  • Data Augmentation: Self-supervised transforms—rotation, cropping, overlay synthetic objects—to expand training data for rare objects.

  • Active Learning Loop: Analysts review low-confidence detections to refine model iteratively.

  • Geospatial Integration: Cross-reference detections with infrastructure, land-use maps, or high-resolution basemaps for context.

  • Deployment Tips: Batch processing pipelines followed by visualization dashboards, with support for tiled imagery and zoom workflows.

Problem Statement 4: RAG-based Question & Answering System

Context: Retrieval-Augmented Generation (RAG) supports semantic search and reasoning across documents to produce coherent summaries and answers. 

Possible Approaches:

  • Embedding Indexing: Use domain-specific corpora (policies, reports, SOPs) to build dense retrievers using fine-tuned BERT-based dual encoders.

  • Chain-of-Thought Reasoning: Retrieve intermediate facts, apply reasoning chains within LLM, and consolidate final answers.

  • Provenance Tracking: For each answer, display sources with confidence scores and citation links to improve transparency.

  • Interactive UI: Allow users to query, trace back rationale, and dive deeper into referenced documents.

  • Deployment Tips: Use vector databases (e.g., Milvus, Pinecone), and secure access to documents with audit logs.

Problem Statement 5: Multi-lingual Document Digitisation

Context: India’s linguistic diversity necessitates OCR systems capable of accurate digitization across multiple languages.

Possible Approaches:

  • Script Detection: Preprocess images to detect script (Devanagari, Tamil, Latin) and route to appropriate OCR model.

  • Transformer-Based OCR: Use encoder–decoder architectures (e.g., Pix2Seq) fine-tuned on multilingual and handwritten samples.

  • Contextual Correction: Feed outputs into language models to correct common errors using grammar and lexicon.

  • Layout and Semantic Analysis: Recognize table formats, stamps, signatures, multiple columns, and use structural parsing to retain layout.

  • Deployment Tips: Package as a plugin or microservice enabling scanned batch processing; allow post-edit feedback to refine accuracy.

Problem Statement 6: Language-Agnostic Speaker ID, Diarization, Transcription & Translation

Context: Supports UI across multiple languages—identify speakers, transcribe speech, and translate.

Possible Approaches:

  • Universal Embeddings: Use x-vectors or ECAPA-TDNN for speaker clustering across varying languages.

  • Voice Language Detection + Routing: Detect language, feed into language-specific ASR modules, followed by translation (or direct end-to-end multi-lingual models like Whisper).

  • Speech Pipeline: Diarize → Transcribe → Translate → Output timeline-aligned transcriptions with speaker tags.

  • Handling Code Switching: Piecewise model detection and mixing via adaptive thresholds.

  • Deployment Tips: Provide cloud or on-premise batch processor; embed into intelligence platforms for multilingual transcription.

Problem Statement 7: Password Extraction & Decryption

Context: Forensic tools often require unlocking password-protected files (documents, disks, compressed archives).

Possible Approaches:

  • Predictive Brute Forcing: Train LLMs with organizational patterns to generate probable password candidates (e.g., project names, dates, user initials).

  • Metadata Clues: Use file timestamps, names, and reuse patterns to prioritize candidate lists.

  • Partial Key Recovery: Use side-channel hints (e.g., file header hints or signature noise) to accelerate decryption.

  • Analyst Portal: Rank possible decryptions by likelihood and display in descending confidence to aid decision-making.

  • Deployment Tips: Integrate securely into forensic workflows, respecting legal boundaries and auditability.

Problem Statement 8: Emitter Location Enhancement

Context: Improve radio emitter localization by reducing the Ellipse Error Probable (EEP) using multi-sensor data.

Possible Approaches:

  • Sensor Fusion: Integrate AoA, TDoA readings using particle or Kalman filters to refine emitter position.

  • Neural Correction Models: Train networks to correct coarse geolocation estimates using historical sensor patterns.

  • Confidence-Based Weighting: Adjust sensor contributions dynamically based on signal quality and environmental conditions.

  • visualization dashboards: Show error ellipses, predicted vs. actual ground truth overlays for analysts.

  • Deployment Tips: Real-time monitoring modules; plug into command-and-control centers with alert thresholds.

Problem Statement 9: Maritime Domain Awareness via SAR/EO Imagery

Context: Detect and classify maritime objects from SAR and EO imagery using multi-sensor inputs. 

Possible Approaches:

  • SAR + EO Fusion: Combine radar’s penetrative ability with optical imagery’s detail via cascaded or early-fusion architectures.

  • Few-Shot Techniques: Use prototypical networks or metric learning to recognize rare vessel types with minimal labeled examples.

  • Tracking Pipelines: Integrate detections across timesteps to track vessel movement, cross-reference with AIS data.

  • Alert System: Highlight suspicious activity, vessel behavior, or anomalies with visual overlays.

  • Deployment Tips: Build as a coastal watch dashboard with periodic data ingestion and auto-alerting.

Problem Statement 10: Change Detection in Satellite Imagery

Context: Detect man-made changes across large landmasses over time using satellite imagery. 

Possible Approaches:

  • Siamese Architecture: Feed multi-temporal images through twin networks to predict changes via feature differences.

  • Temporal Attention: Apply transformers to sequences of images to distinguish seasonal variation from deliberate change.

  • Anomaly Scoring: Highlight changes over thresholds and flag zones for analyst review.

  • Geospatial Mapping: Overlays in maps and time sliders help visualize and trace changes.

  • Deployment Tips: Allow analysts to query by region and time window; scalable cloud-based tiling.

Problem Statement 11: Hyperspectral Anomaly Detection

Context: Detect non-natural (e.g., camouflaged or hidden) changes using hyperspectral data. 

Possible Approaches:

  • Spectral Signature Modeling: Use autoencoders or one-class classifiers trained on “normal” spectra to flag anomalies.

  • Spatial-Spectral Fusion: Use 3D convolutional models to incorporate both pixel-level and context-level features.

  • Active Learning Loops: Analysts verify uncertain hits to refine model iteratively.

  • Visual Interfaces: Feature anomaly heatmaps with spectrum plots for flagged pixels.

  • Deployment Tips: Batch process field data; integrate into remote sensing workflows for further analysis.

Problem Statement 12: Underwater Domain Awareness

Context: Classify underwater acoustic object types (e.g., vessels, submarines) using underwater acoustic signals. 

Possible Approaches:

  • Spectrogram-Based DL: Convert acoustic signals to spectrograms and use CNNs or spectrogram transformers to classify.

  • Transfer Learning from Audio Domains: Leverage pre-trained models (e.g., birdsong, speech recognition) to bootstrap learning.

  • Clustering + Analyst Review: Use unsupervised embeddings to highlight novel acoustic signatures.

  • Real-Time Monitoring: Deploy systems for continuous signal ingestion and alert generation.

  • Deployment Tips: Use edge hardware for collection, paired with backend cloud classification and dashboards.

Conclusion

Each of the 12 NCIIPC problem statements presents a high-impact domain where AI can directly advance national security. From source-code vulnerability detection to maritime and underwater awareness, thoughtful combination of domain knowledge, generative modeling, active learning, and explainability will be key.