Incident response systems

Every company AI adopt pannirukku — chatbots, recommendation engines, fraud detection, code generation. But AI applications secure pannuradhu traditional apps vida completely different! 🤖

AI app la code mattum illai — models, training data, inference pipelines, embeddings — ellam protect pannanum.

2025 la AI-related security incidents 400% increase. Prompt injection attacks, model theft, training data extraction — new attack vectors emerge aagiruku.

OWASP Top 10 for LLMs, AI security best practices, real-world case studies — ellam cover pannurom! Let's secure your AI! 🔒

Traditional app vs AI app — attack surface comparison:

Traditional App Attack Surface:

• Input validation, authentication, authorization
• SQL injection, XSS, CSRF
• Server misconfigurations

AI App — Additional Attack Surface:

Key insight: AI app = Traditional web app security + ML-specific security + Data security + Model security. 4x the attack surface! 😱

Every component separately secure pannanum — one weak link = entire system compromise!

OWASP LLM Top 10 (2025) — every AI developer therinjhukkanum!

LLM01: Prompt Injection 💉

• Direct: User manipulates LLM via crafted input
• Indirect: Hidden instructions in retrieved data
• Impact: Data exfiltration, unauthorized actions

LLM02: Insecure Output Handling 📤

• LLM output directly trust panni execute panradhu
• XSS, command injection via LLM responses
• Always sanitize and validate LLM outputs!

LLM03: Training Data Poisoning ☠️

• Malicious data in training pipeline
• Backdoors, biases, misinformation inject
• Supply chain attack on pre-trained models

LLM04: Model Denial of Service 💣

• Resource-exhausting prompts
• Extremely long inputs, recursive tasks
• Rate limiting and input validation essential

LLM05: Supply Chain Vulnerabilities 📦

• Compromised pre-trained models
• Malicious plugins/extensions
• Poisoned training datasets from third parties

LLM06: Sensitive Information Disclosure 🔓

• Model leaking training data (PII, secrets)
• Membership inference — "was this data in training?"
• Prompt extraction — system prompt reveal

LLM07: Insecure Plugin Design 🔌

• Plugins with excessive permissions
• No input validation on plugin calls
• SQL injection through AI-called tools

LLM08: Excessive Agency 🤖

• AI with too much autonomous capability
• Can execute code, access databases, send emails
• Human-in-the-loop missing for critical actions

LLM09: Overreliance 🧠

• Blindly trusting AI outputs without verification
• AI hallucinations treated as facts
• No human review process

LLM10: Model Theft 🕵️

• Extracting model through API queries
• Side-channel attacks on inference
• Insider threat — employee steals model weights

Prompt injection = AI application ku #1 security threat. Detailed breakdown:

Direct Prompt Injection:

Indirect Prompt Injection:

Real attack scenarios:

Defense strategies:

1. Input sanitization — Strip suspicious patterns
2. System prompt hardening — Clear boundaries, refusal instructions
3. Output filtering — Check responses before returning
4. Privilege separation — AI cannot directly execute sensitive actions
5. Instruction hierarchy — System > User > Retrieved context
6. Canary tokens — Detect if system prompt leaked

AI model is only as good (and secure) as its training data! 📊

Training data threats:

1. Data Poisoning ☠️

• Attack: Malicious data inject into training set
• Impact: Model produces wrong/biased/backdoored outputs
• Defense: Data validation, provenance tracking, anomaly detection

2. Data Leakage 💧

• Attack: Extract training data from model responses
• Impact: PII, trade secrets, copyrighted content exposed
• Defense: Differential privacy, data deduplication, output filtering

3. Supply Chain 📦

• Attack: Compromised datasets from third parties
• Impact: Inherited vulnerabilities and biases
• Defense: Dataset verification, trusted sources only

Secure data pipeline:

Data governance checklist:

• ✅ Data inventory — enna data use pannirukkom?
• ✅ PII identification and masking
• ✅ Data retention policies
• ✅ Access control — who can access training data?
• ✅ Audit trail — data changes tracked
• ✅ Compliance — GDPR, CCPA, AI Act requirements

Model itself oru valuable asset — protect pannanum! 🔐

Model theft prevention:

1. API-level protection:

• Rate limiting — excessive queries block
• Query pattern monitoring — model extraction attempts detect
• Watermarking — model outputs la invisible watermark
• Differential privacy — exact training data extraction prevent

2. Model encryption:

• Weights encryption at rest
• Secure inference with hardware enclaves (Intel SGX, ARM TrustZone)
• Homomorphic encryption — encrypted data la inference (slow but secure)
• Federated learning — data stays distributed

3. Access control:

• Model registry with RBAC
• Signed model artifacts — tamper detection
• Version control for models
• Separate environments: dev / staging / production

Model supply chain security:

Pro tip: Use safetensors format instead of pickle — pickle files can execute arbitrary code during loading! 🚨

⚠️ RAG (Retrieval-Augmented Generation) = Most popular AI pattern, but most vulnerable too!

RAG-specific threats:

1. Data Source Poisoning — Malicious content injected into knowledge base → AI retrieves and uses it → Indirect prompt injection!

2. Unauthorized Data Access — User cleverly asks → AI retrieves documents user shouldn't see → "Tell me about Project X salary data"

3. Context Window Stuffing — Massive documents stuff panni model confuse → Important safety instructions pushed out of context window

4. Embedding Inversion — Vector embeddings reverse panni → original text reconstruct → Sensitive data expose

RAG Security Checklist:

- ✅ Access control on retrieval — User permissions check before returning documents

- ✅ Content sanitization — Retrieved documents la hidden instructions scan

- ✅ Chunk-level permissions — Document level illai, section level access control

- ✅ Query filtering — Sensitive topics detect and block

- ✅ Source attribution — Where did this info come from? Track and display

- ✅ PII filtering — Before response return, PII redact pannunga

Guardrails = AI outputs control panna safety mechanisms. Essential for production AI! 🛡️

Input guardrails:

Output guardrails:

Popular guardrail frameworks:

Key guardrail types:

• 🚫 Topic rails — Off-topic conversations block
• 💉 Injection rails — Prompt injection detect and block
• 🔒 PII rails — Personal information auto-redact
• ⚖️ Factuality rails — Hallucination detect and flag
• 🎭 Jailbreak rails — Role-play and bypass attempts block
• 📏 Format rails — Output format enforce (JSON, etc.)

AI agents = LLMs + tools (code execution, API calls, database access). Most dangerous AI attack surface! ⚠️

Agent security risks:

1. Excessive Permissions 🔑

• Agent has admin database access — prompt injection → DROP TABLE
• Agent can send emails — manipulation → spam/phishing send
• Agent can execute code — exploitation → reverse shell

2. Tool Injection 💉

• Attacker manipulates which tool agent calls
• Parameters tampere panni unintended actions
• Chained tool calls for complex attacks

3. Uncontrolled Autonomy 🤖

• Agent makes decisions without human approval
• Financial transactions, data deletion, API calls
• "I thought the AI said to..." — blame game

Secure agent design:

Rule of thumb: If an AI agent can do it automatically, a prompt injection can make it do it maliciously! Always add human gates for critical operations. 🚪

Production AI systems ku continuous monitoring essential! 👁️

What to monitor:

1. Input Monitoring 📥

• Prompt injection attempts (patterns, frequency)
• Unusual input patterns (automated attacks?)
• PII in user inputs
• Jailbreak attempts categorization

2. Output Monitoring 📤

• Harmful/toxic content generation
• PII leakage in responses
• Hallucination rate tracking
• System prompt leakage detection

3. Model Performance 📊

• Response quality degradation
• Latency spikes (DoS attack?)
• Token usage anomalies
• Error rate changes

4. Cost Monitoring 💰

• Token consumption per user
• API cost anomalies
• Resource utilization spikes
• Billing alerts

Monitoring stack:

Alert thresholds:

• 🔴 Prompt injection detected → Block + alert
• 🔴 PII in output → Redact + log
• 🟡 Cost spike > 200% → Alert team
• 🟡 Error rate > 5% → Investigate
• 🟢 Latency > 5s → Monitor

📋 AI regulations rapidly evolving — compliance is mandatory!

Key regulations:

🇪🇺 EU AI Act (2024)

- Risk-based classification (unacceptable/high/limited/minimal)

- High-risk AI = mandatory conformity assessment

- Transparency requirements for AI-generated content

- Fines: Up to €35M or 7% global revenue

🇺🇸 US Executive Order on AI (2023)

- AI safety testing requirements

- Watermarking for AI-generated content

- Red teaming before deployment

- Dual-use foundation models reporting

🇮🇳 India Digital Personal Data Protection Act

- AI processing of personal data — consent required

- Data localization requirements

- Breach notification mandatory

Compliance checklist for AI apps:

- ✅ AI system classification (risk level)

- ✅ Data processing impact assessment

- ✅ Bias testing and fairness audit

- ✅ Transparency — users know they're talking to AI

- ✅ Human oversight mechanisms

- ✅ Incident response plan for AI failures

- ✅ Documentation of training data sources

- ✅ Regular audits and compliance reviews

Securing AI applications — complete recap:

✅ AI attack surface = Traditional + ML-specific + Data + Model = 4x larger

✅ OWASP LLM Top 10 — Prompt injection is #1 threat

✅ Prompt injection — Direct (user) and Indirect (data) — both defend

✅ Training data — Poisoning, leakage, supply chain risks

✅ Model security — Theft prevention, encryption, signed artifacts

✅ RAG security — Access control on retrieval, content sanitization

✅ Guardrails — Input/output filtering mandatory for production

✅ Agent security — Least privilege, human-in-loop, sandboxing

✅ Monitoring — Continuous observability for AI-specific threats

✅ Compliance — EU AI Act, data protection, transparency

Golden rule: Un AI application ku same security scrutiny kodukka — traditional web app ku kuduppaa adhukku mela! AI apps are powerful but proportionally risky. 🤖🔒

Challenge: Design Complete Incident Response Plan

4 weeks time la organization-level incident response system build pannunga:

1. Incident Response Plan Document — Company profile, asset inventory, threat scenarios. Detection procedures, escalation paths, communication templates create pannunga. Roles and responsibilities clearly define pannunga.

1. IRP Procedures Manual — Detection to recovery steps detailed ah document pannunga. Checklists for different incident types (malware, breach, DDoS, insider threat). Contact information, vendor escalation procedures.

1. Tabletop Exercise — 2-hour simulation run pannunga. Scenario: ransomware attack detected. Teams act out roles, procedures follow pannunga, response time measure pannunga.

1. Forensics Lab Setup — Evidence collection procedures practice pannunga. Disk imaging, memory dump, log preservation techniques learn pannunga.

1. Communication Plan — Internal notification template, external notification (affected users, regulators). Media communication strategy develop pannunga.

1. Recovery Procedures — Backup recovery test pannunga. Disaster recovery plan activate panni restore procedures verify pannunga.

1. Post-Incident Review Template — What happened, why happened, what to prevent. Action items track panni follow-up responsibility assign pannunga.

Certificate: Nee incident response coordinator! 🚨🛡️

Q1: Incident response plan na enna? Why necessary?

A: IRP = structured approach detect, respond, recover from security incidents. Crisis time, plan illama decisions chaotic, costly aagum. Response time reduce pannum (faster containment), damage minimize pannum, learning maximize pannum.

Q2: Detection to response — timeline epdhi manage?

A: MTTD (mean time to detect) = 1-2 hours ideal. MTR (mean time to respond) = 15 mins. MTTR (mean time to recover) = 1-4 hours depending incident. Automation tools SIEM, alerting) detection accelerate pannum. Playbooks response speed increase pannum.

Q3: Forensics evidence collection — critical procedures?

A: Chain of custody maintain pannunga. Volatile data first collect pannunga (memory). Hard drives hash pannunga (bit-for-bit copy). Logs preserve pannunga. Legal compliance ensure pannunga. Reputable forensics firm involve pannunga complex cases la.

Q4: External communication — regulatory notification?

A: Breach notification laws vary (GDPR = 72 hours). Affected customers notify pannunum. Media, investors, regulators communicate pannanum. Transparency important — trust maintain panna. Honest communication crisis manage pannum better.

Q5: Incident response team structure?

A: Incident Commander (coordinates), Technical Lead (investigation), Communications Lead (messaging), Legal (compliance). Availability 24/7 on-call. Regular training, drills, playbook updates. Vendor relationships (forensics, breach coach) pre-established.