Zenity & Amazon Bedrock AgentCore Integration - Maxthon

Executive Summary

The integration between Zenity and Amazon Bedrock AgentCore represents a critical advancement in securing enterprise AI agents at scale. As organizations deploy autonomous AI systems capable of initiating actions, accessing sensitive data, and making business decisions without human oversight, the security gap has become increasingly urgent. This case study examines the integration’s significance, market outlook, detailed solutions architecture, and specific implications for Singapore’s financial services sector and broader economy.

This announcement from Zenity coincides with AWS re:Invent 2025, where Amazon announced new enhancements for Amazon Bedrock AgentCore Yahoo Finance. AgentCore is an agentic platform to build, deploy, and operate highly capable agents securely at scale Amazon Web Services, and it became generally available AWS recently.

What Zenity Offers

Zenity is claiming to be the first to provide full-lifecycle coverage for organizations adopting agentic AI on AWS Yahoo!, addressing a critical gap in AI agent security. The company provides:

Discovery and visibility into which AI agents exist and how they behave across enterprise environments
Real-time threat detection for issues like indirect prompt injection or unauthorized memory access
Policy enforcement to prevent unsafe agent actions
Complete behavioral mapping that traces memory usage, toolchains, and orchestration patterns back to enterprise policies

Why This Matters

AgentCore agents are flexible, code-driven configurations, so AWS does not automatically infer or visualize why an agent uses a particular capability Yahoo!. Zenity fills this security gap by connecting build-time configuration with live runtime telemetry, giving security teams visibility into agent intent and control over agent behavior.

This is particularly important as enterprises face challenges moving from proof of concept to production Constellation Research with AI agents. The integration aims to help organizations scale their agentic AI initiatives while maintaining security, compliance, and governance requirements.

1. Market Context & Problem Statement

The Agentic AI Security Crisis

The enterprise AI landscape in 2025 faces a fundamental security challenge. Research indicates that 82% of organizations already use AI agents, yet only 44% have policies to secure them. This gap creates significant vulnerabilities as AI agents now operate with extensive privileges across enterprise systems.

Key statistics reveal the severity:

80% of organizations report their AI agents have taken unintended actions
39% have experienced unauthorized system access by agents
33% observed agents accessing inappropriate or sensitive data
32% reported agents enabling downloads of sensitive data
23% have been tricked into revealing access credentials through AI agents

The projected economic impact is substantial. Agentic AI is expected to unlock between $2.6 trillion to $4.4 trillion annually in value across more than 60 generative AI use cases. However, this value creation cannot be realized without robust security frameworks.

Why Traditional Security Fails

Traditional application security approaches prove inadequate for AI agents because:

Non-deterministic Behavior: Unlike traditional software following predetermined logic paths, AI agents make contextual decisions that evolve over time
Broad Data Access: Agents can access entire customer databases, integrate with external APIs, and synthesize information across organizational boundaries
Autonomous Decision-Making: Agents operate without human oversight, making them “digital insiders” with significant privilege
Speed and Scale: AI systems operate at machine speed, rendering human-centric identity frameworks insufficient
Novel Attack Vectors: Prompt injection, memory poisoning, tool misuse, and privilege compromise represent entirely new threat categories

Industry analysis shows that 96% of technology professionals consider AI agents a growing risk, and 72% believe they present greater risk than traditional machine identities.

2. The Zenity Solution Architecture

Core Capabilities

Zenity’s integration with Amazon Bedrock AgentCore provides comprehensive, full-lifecycle coverage across four critical dimensions:

2.1 Discovery and Visibility

Challenge: AgentCore’s flexible, code-driven configuration means AWS does not automatically infer or visualize agent capabilities, leaving security teams blind to agent behavior and intent.

Zenity Solution:

Complete inventory of all AI agents across enterprise environments
Real-time behavioral mapping and analysis
Intent analysis that connects capabilities to enterprise policies
Visualization of memory usage patterns, toolchain dependencies, and orchestration flows
Risk scoring based on agent privileges and data access

2.2 Real-Time Threat Detection

Challenge: AI agents can be manipulated through indirect prompt injection, unauthorized memory access, or malicious tool invocation.

Zenity Solution:

Runtime detection of anomalous agent behaviors
Identification of indirect prompt injection attempts
Monitoring for unauthorized memory access patterns
Detection of tool misuse before execution
Behavioral profiling to identify compromised agents

2.3 Inline Enforcement and Policy Control

Challenge: Without proper guardrails, agents can execute unsafe or unsanctioned actions, even as their behaviors evolve autonomously.

Zenity Solution:

Policy-based access controls for agent capabilities
Real-time blocking of unauthorized actions
Function-level validation for tool usage
Role-based access control (RBAC) enforcement
Dynamic authorization based on context and risk levels

2.4 Compliance and Auditability

Challenge: Regulatory frameworks increasingly require explainability, auditability, and governance of AI systems.

Zenity Solution:

Complete audit trails of agent actions and decisions
Traceability from agent behavior back to enterprise policies
Compliance reporting aligned with regulatory requirements
Risk model mapping for governance frameworks
Forensic capabilities for incident investigation

Technical Integration Points

The Zenity-AgentCore integration operates at multiple levels:

Build-Time Security:

Analysis of agent configurations during development
Security recommendations based on agent architecture
Policy template application for common use cases
Risk assessment before deployment

Runtime Protection:

Continuous monitoring of agent telemetry
Real-time threat detection and response
Dynamic policy enforcement based on context
Behavioral analytics for anomaly detection

Post-Deployment Governance:

Comprehensive logging and audit trails
Policy compliance verification
Performance and security metrics
Incident response and remediation

3. Use Cases and Implementation Scenarios

Financial Services

Credit Analysis and Lending:

Agents analyze complex financial documents and client history
Zenity ensures agents cannot exfiltrate sensitive financial data
Policy controls prevent unauthorized changes to credit decisions
Audit trails maintain regulatory compliance

Fraud Detection:

Agents monitor transactions and flag suspicious patterns
Zenity prevents agents from being manipulated to ignore fraud
Memory isolation prevents poisoning of fraud detection models
Tool usage boundaries ensure agents cannot modify transaction records

Healthcare and Life Sciences

Clinical Decision Support:

Agents assist with diagnosis and treatment recommendations
Zenity ensures agents cannot access patient records outside authorization
Policy enforcement prevents agents from making unauthorized changes
Complete audit trails for regulatory compliance (HIPAA, FDA)

Research and Drug Discovery:

Agents analyze research data and suggest experimental approaches
Zenity prevents intellectual property leakage
Tool controls ensure agents cannot modify research databases
Memory protection prevents contamination of research findings

Enterprise Operations

Procurement and Supply Chain:

Agents optimize purchasing decisions and vendor selection
Zenity prevents agents from executing unauthorized purchases
Policy controls ensure compliance with procurement rules
Audit trails track all agent-initiated actions

Human Resources:

Agents assist with candidate screening and employee support
Zenity ensures agents cannot access unauthorized employee data
Policy enforcement prevents discrimination or bias
Complete transparency for HR compliance requirements

4. Market Outlook (2025-2028)

Growth Projections

The AI agent security market is experiencing explosive growth driven by accelerating agentic AI adoption:

Enterprise Adoption:

45% of enterprises currently run at least one production AI agent with access to critical systems (300% increase from 2023)
98% of organizations plan to expand AI agent usage within the next year
79% of senior executives report their organizations are already adopting agentic AI

Market Size:

AI security market projected to grow significantly as organizations recognize the inadequacy of traditional security tools
Specialized AI agent security platforms expected to become mandatory for regulated industries
Integration with cloud platforms (AWS, Azure, Google Cloud) will drive rapid adoption

Competitive Landscape

Current Market Structure:

Traditional security vendors (Palo Alto Networks, CrowdStrike) adding AI security features
Pure-play AI security startups (Zenity, Lasso Security, Robust Intelligence) developing specialized solutions
Cloud providers (AWS, Microsoft, Google) building native security capabilities
Identity management vendors (Okta, SailPoint) extending to AI agent identities

Zenity’s Competitive Position:

First-mover advantage with full-lifecycle AgentCore coverage
Deep integration with AWS ecosystem provides distribution advantage
AWS Marketplace availability accelerates enterprise adoption
Partnership approach positions Zenity as complementary rather than competitive to AWS

Regulatory Drivers

Emerging Requirements:

EU AI Act mandates risk management and transparency for high-risk AI systems
US agencies developing AI governance frameworks (NIST AI RMF, OMB guidance)
Financial regulators (SEC, MAS, FCA) establishing AI model risk management expectations
Data protection authorities extending GDPR/CCPA to AI systems

Compliance Timeline:

2025: Initial regulations taking effect, enforcement beginning
2026: Comprehensive AI governance requirements across major jurisdictions
2027: Regular AI audits becoming standard practice
2028: AI security certifications becoming competitive requirements

Technology Evolution

Near-Term (2025-2026):

Integration of AI security with existing security operations centers (SOCs)
Automated policy generation based on agent behavior analysis
Enhanced explainability capabilities for compliance requirements
Cross-platform agent security management

Medium-Term (2027-2028):

AI-powered security agents defending against malicious AI agents
Self-healing security systems that adapt to new threats
Industry-specific security frameworks and compliance templates
Standardization of AI agent security protocols and best practices

5. Singapore Market Impact and Opportunities

Current AI Landscape in Singapore

Singapore has positioned itself as a global leader in responsible AI adoption through coordinated government strategy and strong industry execution.

Government Initiatives:

National AI Strategy 2.0 driving systematic AI adoption across economy
SGD 1 billion investment over five years in AI computing resources, talent, and industry development
SGD 150 million Enterprise Compute Initiative (announced February 2025) providing cloud services access
Model AI Governance Framework providing practical guidance for AI deployment

Financial Services Leadership:

Monetary Authority of Singapore (MAS) leading through AIDA (AI and Data Analytics) program
Over 30 financial institutions have established AI functions in Singapore
DBS Bank generated SGD 750 million in AI value in 2024, projecting over SGD 1 billion in 2025
OCBC reports AI powering 4 million decisions daily
More than 50 innovation centers established by financial institutions

Regulatory Framework:

AI Verify: World’s first AI governance testing framework and toolkit
Veritas Framework for financial AI governance
FEAT principles (Fairness, Ethics, Accountability, Transparency)
PathFinder program supporting financial institutions in AI adoption
Global AI Assurance Pilot launched February 2025

Singapore’s Unique Position for Zenity Adoption

Factor 1: High Regulatory Maturity

Singapore’s emphasis on governance-first AI adoption creates ideal conditions for Zenity:

Financial institutions must comply with MAS guidance on AI model risk management
Model Framework requirements for explainability, transparency, and fairness align with Zenity capabilities
AI Verify testing requirements necessitate third-party validation tools
Strong data protection framework (PDPA) extends to AI systems

Factor 2: Financial Services Concentration

Singapore’s status as a regional financial hub creates significant demand:

Over 120 commercial banks operating in Singapore
Leading position in wealth management, trade finance, and insurance across Asia
Regional headquarters for major global banks (DBS, UOB, OCBC, plus international banks)
Concentration of fintech innovation and AI-first financial services

Factor 3: AWS Market Penetration

Singapore’s cloud infrastructure landscape favors AWS partnerships:

AWS has significant presence with multiple availability zones
Government agencies increasingly adopting AWS for digital services
Financial institutions leveraging AWS for core systems and innovation
Strong AWS partner ecosystem supporting enterprise implementations

Factor 4: Smart Nation Initiative Alignment

Singapore’s broader digital transformation strategy supports AI agent adoption:

Government commitment to technology-driven efficiency
Public sector exploring agentic AI for citizen services
Healthcare system adopting AI for clinical decision support
Smart city initiatives deploying autonomous systems

Specific Use Cases for Singapore

Banking and Financial Services

Wealth Management:

AI agents providing personalized investment advice to high-net-worth clients
Zenity ensuring agents cannot leak client financial information
Compliance with MAS requirements for advisory services
Audit trails for regulatory reporting

Trade Finance:

Agents processing complex trade documentation and compliance checks
Zenity preventing manipulation of trade documents
Policy enforcement for sanctions and AML requirements
Real-time monitoring of cross-border transactions

Insurance Underwriting:

Agents analyzing risk factors and pricing policies
Zenity ensuring fair and unbiased underwriting decisions
Protection against fraudulent claim manipulation
Regulatory compliance for insurance regulations

Healthcare

Clinical Operations:

Agents supporting diagnosis in Singapore’s advanced healthcare system
Protection of patient data under PDPA requirements
Compliance with Medical Device Act for AI-Medical Devices
Integration with Ministry of Health AI in Healthcare Guidelines

Pharmaceutical Research:

Singapore’s biomedical sciences hub deploying agents for drug discovery
Protection of intellectual property and research data
Compliance with international research standards
Prevention of research data manipulation

Government and Smart City

Citizen Services:

Government agencies deploying agents for public service delivery
Protection of citizen data and privacy
Transparency requirements for government AI systems
Accountability for automated decisions affecting citizens

Infrastructure Management:

Smart Nation sensors and systems using AI agents
Security for critical infrastructure systems
Prevention of manipulation affecting public services
Compliance with Cybersecurity Act requirements

Market Entry Strategy for Singapore

Phase 1: Financial Services (Months 1-6)

Target Accounts:

Local banks: DBS, UOB, OCBC
International banks: HSBC, Standard Chartered, Citibank
Fintech companies and digital banks

Approach:

Leverage AWS Marketplace for procurement simplicity
Partner with AWS Professional Services for implementations
Align messaging with MAS AIDA program objectives
Demonstrate compliance with Veritas Framework

Value Proposition:

Reduce AI model risk management burden
Accelerate compliant AI agent deployment
Enable expansion of AI use cases with confidence
Provide regulatory audit readiness

Phase 2: Healthcare and Life Sciences (Months 6-12)

Target Accounts:

Public healthcare clusters
Private hospital groups
Pharmaceutical companies with R&D operations
Medical technology companies

Approach:

Partner with healthcare technology consultants
Align with MOH AI in Healthcare Guidelines
Demonstrate PDPA compliance capabilities
Showcase clinical decision support use cases

Phase 3: Government and Enterprise (Months 12-18)

Target Accounts:

Government Technology Agency (GovTech)
Statutory boards and ministries
Large enterprises across sectors

Approach:

Participate in government procurement frameworks
Demonstrate Smart Nation alignment
Partner with system integrators
Showcase public sector use cases from other regions

Investment and Partnership Opportunities

AWS Partnership Expansion

AWS Marketplace co-selling motions
Joint reference architectures for regulated industries
Co-marketing through AWS Singapore events (re:Invent APAC, etc.)
Integration with AWS Control Tower for governance at scale

Local Technology Partners

Accenture, Deloitte, PwC Singapore for implementation services
Local system integrators (NCS, ST Engineering) for government sector
Singapore Computer Society for professional training and certification
AI Singapore for research collaboration and talent development

Academic and Research Collaboration

National University of Singapore (NUS) for AI security research
Nanyang Technological University (NTU) for applied research projects
Singapore Management University (SMU) for business school engagement
AI Verify Foundation for standards development contribution

Government Engagement

IMDA collaboration on AI governance frameworks
MAS engagement through PathFinder program
Participation in AI Verify Foundation as general member
Contribution to Singapore’s AI safety research priorities

Economic Impact Projections

Direct Revenue Potential (2025-2028):

Financial Services: SGD 15-25 million annually by 2028
Healthcare/Life Sciences: SGD 5-10 million annually by 2028
Government/Enterprise: SGD 5-8 million annually by 2028
Total Singapore Market: SGD 25-43 million annually by 2028

Broader Economic Benefits:

Enable safer deployment of AI agents across Singapore’s economy
Support Singapore’s goal of becoming global AI hub
Create local jobs in AI security specialization
Attract international AI companies seeking secure deployment region
Strengthen Singapore’s position as trusted financial center

Competitive Positioning:

First-mover advantage in Singapore’s agent security market
Strong alignment with government AI strategy
Deep integration with leading cloud platform
Regulatory compliance enablement as key differentiator

6. Implementation Roadmap and Best Practices

Phase 1: Assessment and Planning (Weeks 1-4)

Objectives:

Complete inventory of existing AI agents and use cases
Identify high-risk agents requiring immediate security controls
Map agent capabilities to enterprise policies
Define security and compliance requirements

Activities:

Deploy Zenity discovery tools across environment
Conduct risk assessment of current agent deployments
Review organizational AI governance policies
Define success metrics and KPIs

Deliverables:

AI agent inventory and risk register
Security gap analysis report
Implementation roadmap
Resource and budget plan

Phase 2: Pilot Implementation (Weeks 5-12)

Objectives:

Implement Zenity security for 3-5 high-priority agents
Validate effectiveness of security controls
Train security team on Zenity platform
Refine policies and configurations

Activities:

Deploy Zenity runtime protection for pilot agents
Configure policies for specific use cases
Establish monitoring and alerting processes
Conduct simulated attack scenarios
Train security operations team

Deliverables:

Pilot implementation report with metrics
Refined security policies and playbooks
Training materials and documentation
Executive briefing on results

Phase 3: Enterprise Rollout (Weeks 13-26)

Objectives:

Extend Zenity protection to all production agents
Integrate with existing security operations
Establish ongoing governance processes
Enable self-service for development teams

Activities:

Phased rollout to remaining agent populations
Integration with SIEM and security orchestration tools
Development of agent security playbooks
Creation of policy templates for common use cases
Establishment of agent security review board

Deliverables:

Full enterprise protection coverage
Integrated security operations workflows
Agent security governance framework
Continuous improvement process

Phase 4: Optimization and Scaling (Ongoing)

Objectives:

Continuously improve agent security posture
Expand to new agent use cases and platforms
Maintain compliance with evolving regulations
Share best practices across organization

Activities:

Regular security assessments and red team exercises
Policy optimization based on operational experience
Expansion to new business units and use cases
Participation in industry working groups
Contribution to internal and external knowledge base

Deliverables:

Quarterly security posture reports
Updated policies and procedures
Case studies and best practice documentation
Regulatory compliance certifications

Critical Success Factors

Executive Sponsorship:

Strong leadership commitment from CISO and CIO
Board-level awareness of AI security risks
Adequate budget allocation for tools and resources
Clear accountability for AI security outcomes

Cross-Functional Collaboration:

Security, development, and business teams working together
Shared understanding of AI risks and controls
Integrated workflows avoiding siloed approaches
Common language and frameworks

Continuous Learning:

Regular training on evolving AI threats
Participation in industry forums and working groups
Sharing of threat intelligence and best practices
Investment in team skill development

Metrics and Accountability:

Clear KPIs for agent security effectiveness
Regular reporting to leadership and board
Accountability for security incidents and near-misses
Continuous improvement based on data

7. Risk Mitigation and Governance

Key Risk Categories and Controls

Operational Risks

Agent Availability:

Risk: Security controls impacting agent performance or availability
Mitigation: Careful policy design, extensive testing, graduated rollout
Monitoring: Performance metrics, SLA tracking, user feedback

False Positives:

Risk: Security controls blocking legitimate agent actions
Mitigation: Behavioral learning, policy tuning, exception processes
Monitoring: Alert analysis, user complaints, business impact assessment

Security Risks

Policy Bypass:

Risk: Sophisticated attacks circumventing security controls
Mitigation: Defense-in-depth approach, regular red team exercises, threat intelligence
Monitoring: Anomaly detection, security research, incident analysis

Insider Threats:

Risk: Authorized users manipulating agents or security policies
Mitigation: Separation of duties, audit logging, policy review processes
Monitoring: User behavior analytics, privileged access reviews, audit trail analysis

Compliance Risks

Regulatory Changes:

Risk: Evolving regulations requiring security control updates
Mitigation: Regular regulatory monitoring, flexible policy framework, expert engagement
Monitoring: Regulatory change tracking, compliance assessments, industry participation

Audit Readiness:

Risk: Inability to demonstrate compliance during audits
Mitigation: Comprehensive logging, documentation, regular self-assessments
Monitoring: Audit trail completeness, policy documentation, compliance reporting

Governance Framework

AI Security Steering Committee:

Membership: CISO, CIO, Chief Risk Officer, business leaders, legal counsel
Responsibilities: Strategic direction, policy approval, resource allocation, risk oversight
Cadence: Quarterly meetings with ad-hoc sessions for critical issues

Agent Security Review Board:

Membership: Security architects, data protection officers, compliance specialists
Responsibilities: Review new agent deployments, approve high-risk use cases, policy exceptions
Cadence: Bi-weekly meetings with expedited review process for urgent requests

Security Operations Team:

Responsibilities: Day-to-day monitoring, incident response, policy tuning, reporting
Structure: 24/7 coverage integrated with existing security operations center
Skills: AI/ML understanding, security analysis, incident response, forensics

8. Conclusion and Strategic Recommendations

Key Takeaways

Security Gap is Real and Growing: With 82% of organizations using AI agents but only 44% having security policies, the risk exposure is significant and expanding rapidly
Traditional Security is Insufficient: AI agents require specialized security approaches that account for autonomous decision-making, broad data access, and novel attack vectors
Zenity-AgentCore Integration Addresses Critical Need: Full-lifecycle coverage from development through runtime provides comprehensive protection that competitors cannot match
Singapore Presents Ideal Market: Regulatory maturity, financial services concentration, AWS adoption, and Smart Nation initiative create exceptional conditions for success
Time to Act is Now: First-mover advantages and growing regulatory requirements make immediate implementation strategically critical

Strategic Recommendations for Organizations

Immediate Actions (Next 30 Days):

Conduct comprehensive inventory of AI agents across environment
Assess current security gaps and compliance exposures
Prioritize high-risk agents for immediate protection
Engage with Zenity and AWS for technical discussions

Near-Term Initiatives (Next 90 Days):

Implement pilot deployment on critical agent use cases
Establish AI security governance framework and processes
Train security team on agent-specific threats and controls
Develop roadmap for enterprise-wide rollout

Long-Term Strategy (Next 12 Months):

Achieve comprehensive protection across all production agents
Integrate agent security into enterprise security architecture
Build internal expertise in AI security operations
Contribute to industry standards and best practices

The Imperative for Action

The convergence of rapidly expanding AI agent deployments, increasingly sophisticated threats, and evolving regulatory requirements creates an urgent imperative for action. Organizations that delay implementing comprehensive agent security face significant risks: data breaches, compliance violations, reputational damage, and loss of competitive advantage.

Conversely, organizations that proactively implement robust agent security position themselves to:

Safely accelerate AI adoption and innovation
Meet regulatory requirements with confidence
Build trust with customers and stakeholders
Achieve competitive differentiation through secure AI capabilities

The Zenity and Amazon Bedrock AgentCore integration provides a proven path forward, combining AWS’s powerful agent development platform with Zenity’s comprehensive security capabilities. For Singapore’s leading organizations, this represents not just a technology decision, but a strategic imperative in the race to harness AI’s transformative potential while managing its inherent risks.

The future belongs to organizations that can deploy AI agents at scale with confidence in their security, governance, and compliance. The time to build that capability is now.

Appendix: Additional Resources

Technical Documentation

Amazon Bedrock AgentCore Developer Guide
Zenity Security Platform Architecture
Integration Configuration Templates
Security Policy Examples

Regulatory Frameworks

Singapore Model AI Governance Framework
MAS Veritas Framework for Financial AI
AI Verify Testing Framework
FEAT Principles Implementation Guide

Industry Research

McKinsey: “Deploying Agentic AI with Safety and Security”
Gartner: Enterprise AI Agent Adoption Trends
SailPoint: AI Agent Risk Survey Results
Obsidian Security: AI Security Landscape Analysis

Contact Information

Zenity Sales: [Contact through AWS Marketplace]
AWS AgentCore Support: [AWS Support Portal]
Singapore Implementation Partners: [Regional Partner Directory]
Regulatory Guidance: [MAS AIDA Program Office]