Skip to content
đź’Ľ

Executive Guidance

Strategic security guidance for CISOs and security leaders. Compliance frameworks, risk quantification, and board-ready reporting for AI agent deployments.

AI Agent Security for Leaders

AI agents are rapidly moving from experimental projects to production deployments. Security leaders need to understand the unique risks these systems introduce and how to govern them effectively.

Executive Summary

What’s Different About AI Agent Security?

Traditional ApplicationsAI Agents
Deterministic behaviorNon-deterministic outputs
Static code pathsDynamic code generation
Well-defined APIsNatural language interfaces
Clear audit trailsComplex decision chains
Predictable costsVariable compute consumption

Key Risks for the Business

  1. Financial - Unbounded API costs from token bombing attacks
  2. Operational - System outages from infinite loops
  3. Data - Exfiltration through prompt injection
  4. Reputational - Harmful outputs reaching customers
  5. Compliance - Regulatory violations from autonomous decisions

Governance Framework

AI Agent Security Policy

Every organization deploying AI agents should establish:

Acceptable Use

  • Approved use cases and prohibited activities
  • Human oversight requirements
  • Data handling restrictions

Development Standards

  • Secure coding requirements
  • Mandatory security scanning
  • Code review requirements

Deployment Controls

  • Pre-production security gates
  • Runtime monitoring requirements
  • Incident response procedures

Compliance Mapping

EU AI Act

The EU AI Act introduces requirements for AI systems based on risk classification:

Risk LevelAgent ExamplesRequirements
High RiskAgents in healthcare, finance, HRConformity assessment, logging, human oversight
Limited RiskCustomer service botsTransparency obligations
Minimal RiskInternal productivity toolsBest practices recommended

Key Obligations for High-Risk Systems:

  • Risk management system
  • Data governance
  • Technical documentation
  • Logging and traceability
  • Human oversight mechanisms
  • Accuracy, robustness, cybersecurity

NIST AI Risk Management Framework

NIST AI RMF provides a structured approach to AI risk management:

GOVERN - Establish AI governance structures MAP - Identify and document AI system risks MEASURE - Assess and track risks over time MANAGE - Prioritize and address identified risks

SOC 2 Considerations

AI agents introduce new considerations for SOC 2 compliance:

  • Security - How are agent actions controlled and monitored?
  • Availability - What happens when agents fail or loop?
  • Processing Integrity - How do you verify agent outputs?
  • Confidentiality - How is sensitive data protected from agents?

Risk Quantification

Calculating AI Agent Risk

Token Bombing Impact:

Cost Risk = (Max Tokens/Hour Ă— Token Cost) Ă— Hours Until Detection
Example: (1M tokens/hr Ă— $0.01/1K) Ă— 4 hours = $40,000

Data Breach Exposure:

Exposure = Documents Accessible Ă— Sensitivity Score Ă— Exploitation Probability

Security Metrics for AI Agents

MetricDescriptionTarget
MTTDMean time to detect agent anomalies< 5 minutes
Token Budget UtilizationActual vs allocated token usage< 80%
Iteration Limit HitsFrequency of agents hitting limits< 1% of sessions
Security Scan Coverage% of agent code scanned100%
Vulnerability DensitySecurity findings per agent< 2 high/critical

Board-Ready Materials

One-Slide Summary

AI Agent Security Posture

âś… Controls Implemented

  • Mandatory security scanning in CI/CD
  • Token budgets and rate limiting
  • Runtime anomaly detection
  • Incident response playbook

⚠️ Areas of Focus

  • Expanding coverage to new agent frameworks
  • Improving detection of novel attack patterns
  • Regulatory compliance preparation

đź“Š Key Metrics

  • 100% of agents scanned before deployment
  • 0 critical vulnerabilities in production
  • < 5 minute mean time to detect anomalies

Implementation Roadmap

Phase 1: Foundation (0-30 days)

  • Inventory all AI agents in development and production
  • Implement basic token budgets and rate limits
  • Deploy Inkog scanner in CI/CD pipeline
  • Establish incident response procedures

Phase 2: Maturity (30-90 days)

  • Implement runtime monitoring and alerting
  • Develop comprehensive testing procedures
  • Train development teams on secure agent patterns
  • Document compliance mappings

Phase 3: Excellence (90+ days)

  • Continuous security testing and red teaming
  • Advanced anomaly detection
  • Regular third-party assessments
  • Industry benchmarking

Questions from the Board

“How do we know our AI agents are secure?”

We implement defense-in-depth:

  1. Static analysis scanning catches vulnerabilities before deployment
  2. Runtime monitoring detects anomalies in production
  3. Token budgets and rate limits prevent runaway costs
  4. Regular penetration testing validates our controls

“What’s our exposure if an agent is compromised?”

We’ve implemented blast radius controls:

  • Agents operate with least-privilege permissions
  • Data access is segmented by sensitivity
  • Automatic circuit breakers limit damage
  • We can kill an agent’s access in < 1 minute

“Are we compliant with emerging AI regulations?”

We’re actively preparing:

  • Mapped our agents to EU AI Act risk categories
  • Implementing required logging and oversight
  • Documentation ready for conformity assessments
  • Tracking regulatory developments

Next Steps