🔍 Explainable AI in Cybersecurity: Making Defence Decisions Transparent and Trustworthy

As organisations increasingly deploy AI for security operations—ranging from threat detection to incident response—the opacity of machine learning models poses a critical risk. In cybersecurity, where milliseconds matter and every false positive or missed anomaly could cost millions, understanding why AI makes a certain decision is essential for trust, auditability, and effective action.

Why Cybersecurity Needs Explainable AI

Cybersecurity AI systems ingest terabytes of structured and unstructured data—logs, network traffic, endpoint signals, emails—to detect threats and anomalies. These systems often use complex models like Random Forests, Deep Neural Networks, or Unsupervised Clustering techniques.

While these models offer high accuracy, their lack of transparency can create challenges in:

• Justifying security actions to leadership
• Conducting forensic investigations post-incident
• Proving regulatory compliance
• Enabling SOC analysts to trust and act on alerts

Without explanation, AI-driven security decisions become blind spots in your cyber defence posture.

Key Use Cases of Explainable AI in Cybersecurity

Tools and Techniques for XAI in Cybersecurity

1. SHAP (Shapley Additive Explanations)

Used to assign weighted importance to features (e.g., login time, file access frequency).

🔐 Example: SHAP can explain why a user’s login behaviour was classified as “suspicious” due to after-hours access from an unusual IP and rare command-line usage.

2. LIME (Local Interpretable Model-Agnostic Explanations)

Generates human-understandable explanations for individual alerts.

🔐 Example: LIME can clarify why a specific network packet was flagged, identifying packet size and destination IP as key factors.

3. ELI5

Explains linear classifiers and decision trees used in simple intrusion detection systems.

4. Integrated Gradients & Attention Maps

Primarily for deep learning-based malware detection, where explainability is visualised.

Case Study: Banking Sector

Scenario: A major bank deployed an AI model to detect account takeovers through behavioural biometrics.

• Challenge: The model flagged VIP accounts for fraud, causing reputational risks.
• Solution: Implemented SHAP to reveal that keystroke dynamics and mouse patterns were the drivers—not transactional behaviour.
• Outcome: Enabled analysts to validate the model’s logic, reduce false flags by 36%, and preserve trust with high-value clients.

Below are realistic and insightful case studies tailored for:

• Indian MSMEs (specific to industry types common in India),
• Estonian SMEs, and
• Broader European SMEs (especially under GDPR and EU AI Act influence),

all focused on the practical use of Explainable AI (xAI) in cybersecurity.

🇮🇳 Case Study 1: Indian MSME – Manufacturing Sector

Company: ShaktiTech Gears Pvt Ltd (fictitious)

Sector: Auto-component Manufacturing

Size: 80 employees

Digital Footprint: ERP, email systems, IoT-enabled CNC machines

Cyber Challenge: Frequent phishing attempts targeting procurement and finance teams.

Problem

An AI-based email filtering solution flagged dozens of emails weekly as “potential phishing,” leading to confusion among non-technical staff. A few key invoices were blocked mistakenly, delaying vendor payments and damaging business relationships.

xAI Deployment

The IT service provider integrated LIME-based explainability into the email filtering dashboard. Each alert now includes a human-readable explanation like:

• “The sender’s domain does not match historical patterns”
• “The invoice filename resembles patterns from known phishing campaigns”

Outcome

• 46% reduction in false positives within the first month
• Procurement staff trained to read xAI-generated justifications
• Improved confidence in email filters, leading to faster and safer decision-making
• Helped meet internal IT audit criteria set by a new global client

🇮🇳 Case Study 2: Indian MSME – EdTech SaaS Provider

Company: LearnFast Academy Pvt Ltd

Sector: Online Learning Platform

Size: 25 employees

Cyber Challenge: Unusual student login patterns triggering fraud alerts

Problem

The AI-based fraud detection system flagged students logging in from hostels, cyber cafés, or mobile hotspots as suspicious. Parent complaints and refund requests began to rise due to account lockdowns.

xAI Integration

Implemented SHAP with LightGBM to explain each fraud detection. The dashboard now shows weighted reasons such as:

• “Access from unknown device and sudden score jumps on weekly quizzes”
• “3 failed logins from IP address linked to proxy services”

Outcome

• False positives reduced by 58%
• IT team tuned risk thresholds based on explainability feedback
• No further parent escalations in the next quarter
• Boosted investor confidence before Series A funding

🇪🇪 Case Study 3: Estonian SME – LegalTech Startup

Company: eClause OÜ

Sector: Contract Analytics (Legal AI)

Size: 10 employees

Cyber Challenge: Regulatory requirement to explain AI behaviour under GDPR and EU AI Act.

Problem

Their legal document scanner flagged certain clauses as “high-risk,” but offered no insight into why. Clients (legal teams) demanded transparency to understand algorithmic risk scoring.

xAI Implementation

• Integrated ELI5 and SHAP into their AI contract risk scoring tool
• Added a panel for legal users showing which terms, wordings, or structure contributed to the risk score

Outcome

• Legal clients gained confidence in AI recommendations
• Successfully passed a client-side GDPR AI audit
• Received a grant from the Estonian Business and Innovation Agency (EISA) under AI compliance category

🇪🇺 Case Study 4: European SME – E-Commerce Logistics (Netherlands)

Company: FastRoute BV

Sector: E-commerce Logistics & Last-Mile Delivery

Size: 60 employees across 2 cities

Cyber Challenge: AI model blocked parcel label generation during peak hours due to suspected bot activity

Problem

During festive seasons, automated parcel label requests triggered an AI model to restrict suspicious high-volume traffic. Legitimate clients faced delivery delays.

xAI Solution

Used Integrated Gradients to explain DNN decisions behind bot detection. IT staff added a quick-review feature showing:

• “Label requests from the same subnet in < 2 seconds”
• “No user agent string present – typical bot signature”
• “Mismatch in session token validity”

Outcome

• Enabled white-listing of trusted APIs used by premium clients
• Resolved business impact issue without downgrading security
• Featured in a Dutch cyber innovation newsletter for explainable AI use

📌 Summary Table

Risk Mitigation through XAI

Strategic Recommendations for CISOs and AI Leaders

1. Mandate XAI in Procurement: Demand explainability as a non-negotiable requirement in AI-based security tools.
2. Embed XAI in SIEM/SOAR Workflows: Visualise reasoning behind incident scores, triage classifications, and escalations.
3. Upskill Security Analysts: Train SOC teams to read and interpret SHAP/LIME outputs alongside traditional logs.
4. Monitor Model Evolution: Track shifts in what features influence alerts to detect bias or model degradation.
5. Establish XAI Governance: Create policies for acceptable model behaviours and justification thresholds.

🇮🇳 Explainable AI (xAI) in Indian MSMEs: Democratising Trust in Cybersecurity

⚙️ Context

India’s 63 million+ MSMEs contribute ~30% to GDP and face rising cyber threats, especially with growing digital payments, remote work, and cloud adoption. However, awareness and adoption of advanced AI systems—let alone explainable AI—is still nascent.

💡 Why xAI Matters for Indian MSMEs

🚀 Practical Implementation

• Leverage affordable open-source xAI tools (like SHAP with LightGBM models)
• Integrate xAI into low-code/no-code cybersecurity dashboards
• Collaborate via cluster-based CERTs or industry bodies (e.g., NASSCOM, DSCI) for shared threat intelligence + explainability frameworks

🔖 MSME Incentive Schemes to Watch

• Cyber Surakshit Bharat (under MeitY): May integrate explainability clauses in future AI-based tool funding
• SIDBI-funded digital transformation initiatives could recommend Explainable AI for high-risk sectors (FinTech, Logistics, Health)

🔍 Explainable AI Across the Cybersecurity Lifecycle

As AI becomes embedded in nearly every cyber defence mechanism, explainability is critical for security teams to validate actions, defend decisions, and triage threats with speed and trust. Below is how xAI enhances visibility, reduces risk, and enables compliance across each critical stage of security operations.

1️⃣ xAI for Reconnaissance Detection (Counter-Recon)

Use Case

• Detecting external scanning, subdomain enumeration, OSINT probing, or automated crawling.

xAI Role

• AI models that classify reconnaissance behaviour (based on patterns in traffic, timing, or headers) can use SHAP or LIME to explain:
  • Why was a scanning IP flagged?
  • Which specific probe (e.g., Nmap fingerprint, HTTP header anomaly) triggered the alert?

Benefits

• Helps security analysts distinguish benign scanning (e.g., search engines) from malicious intent.
• Improves threat intel enrichment and legal evidence building against persistent threats.

2️⃣ xAI in Vulnerability Assessment (VA)

Use Case

• AI-driven scanners prioritise thousands of vulnerabilities using CVSS scores + custom risk models.

xAI Role

• Feature importance (SHAP) explains why certain vulnerabilities are ranked higher:
  • Exploit maturity
  • Business context (e.g., exposed payroll server)
  • Historical breach association

Benefits

• SOC analysts and CISOs gain visibility into prioritisation logic.
• Justifies mitigation strategies during board presentations or ISO/PCI audits.

3️⃣ xAI in Vulnerability Management

Use Case

• Managing large inventories of vulnerabilities across hybrid assets (cloud, on-prem, SaaS).

xAI Role

• Explainability enables security teams to answer:
  • Why is this vulnerability recurring?
  • What factors contribute to risk ranking changes?
• Tracks model drift: if the AI starts deprioritising critical vulns due to changing data patterns.

Benefits

• Increases transparency in remediation SLA decisions.
• Enables continuous feedback loop between IT ops, DevSecOps, and CISO stakeholders.

4️⃣ xAI in Patch Management

Use Case

• AI models recommend or automate patching windows based on business usage patterns, CVE timelines, and vendor trust scores.

xAI Role

• Explains:
  • Why patch X is urgent (e.g., zero-day exploit, public POC)
  • Why patch Y can wait (e.g., not exposed to internet, low privilege escalation impact)

Benefits

• Avoids over-patching and downtime in production environments.
• Supports decisions with auditable reasoning for patch deferment.

5️⃣ xAI for External Attack Surface Management (EASM)

Use Case

• AI discovers shadow IT, leaked credentials, misconfigured public assets.

xAI Role

• Explains:
  • Why was a subdomain flagged as risky?
  • What behaviour indicated credential exposure on paste sites or forums?

Techniques

• LIME/SHAP explain what patterns in DNS records, IP ownership history, or web stack fingerprinting led to classification.

Benefits

• Validates AI findings before triggering takedown or escalation.
• Helps non-technical stakeholders understand the scope of exposure.

6️⃣ xAI in Dark Web Monitoring

Use Case

• Detecting leaked employee credentials, intellectual property mentions, or targeted chatter in deep/dark web forums.

xAI Role

• xAI models explain:
  • Which data elements triggered the match (e.g., email syntax, contextual co-occurrence)?
  • Why this forum post was rated high risk?

Benefits

• Prevents alert fatigue by contextualising threats.
• Supports legal & compliance teams in evaluating data breach materiality.

7️⃣ xAI in Penetration Testing (PT) and Red Teaming

Use Case

• AI-enhanced pentesting frameworks (e.g., automated exploit chains, payload generation).

xAI Role

• Explains:
  • Why did the AI choose a particular exploit path?
  • What vulnerability chaining logic was applied?

Application

• Used in red team debriefs to explain simulated kill-chains.
• Enables defensive teams to learn and build compensating controls.

Benefits

• Enhances knowledge transfer from red to blue teams.
• Helps clients understand attacker logic, not just outcomes.

📊 Summary Table

✅ CISO Guidance

• Embed xAI outputs in CTEM dashboards: Each detection or recommendation should include explainable logic for boardroom review.
• Mandate xAI capability in RFPs for VAPT/EASM/VA tools: Ensure vendors support SHAP/LIME outputs or custom explanation layers.
• Use xAI to support cyber insurance justifications: Insurers are beginning to demand proof of explainability in AI decisions post-incident.

🇪🇪 Explainable AI (xAI) in Estonian SMEs: A Digital Nation’s Trust Imperative

🌐 Context

Estonia is one of the most advanced digital societies. With over 90% of government services online, SMEs are both targets and defenders in a digitally cohesive economy. Cybersecurity is a national priority, but AI adoption still remains experimental among SMEs.

🧠 Why xAI Is a Strategic Enabler

🇪🇺 EU AI Act Impact

• SMEs must prove risk transparency if they develop or use high-risk AI tools (e.g., for biometric security, credit scoring, critical infrastructure).
• xAI helps SMEs document model behaviour and response decisions, avoiding non-compliance penalties.

🔧 Implementation Strategy

• Promote xAI-as-a-Service platforms embedded into SIEM/XDR tools
• Encourage use of public sector–driven explainability frameworks (via eGA or EU Horizon grants)
• Partner with local cybersecurity startups to integrate modular xAI toolkits into MSP offerings

📊 Comparison Table – xAI Priorities for MSMEs vs SMEs

🏁 Strategic Recommendations

For Indian MSMEs:

• Embed xAI in basic cyber hygiene offerings—MFA, endpoint monitoring, phishing simulations
• Leverage xAI outputs in vendor risk assessments, especially when dealing with global partners
• Train MSME IT staff in interpreting SHAP/LIME with vernacular tool support (Kannada, Hindi, etc.)

For Estonian SMEs:

• Incorporate xAI models into national e-residency tools for secure access control
• Co-develop explainability plugins with cybersecurity startups + TalTech research labs
• Promote public-private knowledge exchange (e.g., via NATO CCDCOE, EU AI Policy Labs)

Final Thoughts

In cybersecurity, AI without explainability is like a locked vault with no combination. It might hold answers, but they remain inaccessible. Explainable AI not only enhances security accuracy but also supports human intelligence, enabling joint decision-making between man and machine.

In an era of sophisticated cyber threats, explanation is your last line of defence—and often your first step towards recovery.