- AI & Data Security
Protect what matters most. Your AI, your data, and your business.
NRI’s AI & Data Security combines adversary simulation, structured testing, and strategic planning to uncover risk,
validate controls, and strengthen resilience across AI systems, data environments, and backup infrastructure.
- Governance, Risk and Compliance
Governance with confidence. Compliance with clarity.
In an era of evolving risks and shifting regulations, our Governance, Risk and Compliance (GRC) solutions provide the resilience, accountability, and strategic advantage needed to stay ahead.
AI has created a new attack surface, one that traditional security testing cannot fully address. Risk no longer lives only in infrastructure or applications.
It spans models, prompts, data, identities, and autonomous workflows, all interacting in ways that are difficult to predict and even harder to secure
NRI’s AI Red Teaming & Testing delivers a comprehensive approach:
- Simulate real-world attacks against AI systems and workflows
- Systematically test controls and guardrails for consistency and effectiveness
- Validate business impact to prioritize what matters most
The result: clear actionable recommendations to reduce your AI risk that will enable the organization to accelerate innovation while maintaining trust and control.
Why AI & Data Security Needs to Change
Traditional security approaches treat AI, data, and backup as separate domains. Attackers do not.
Modern threats exploit the connections between:
- AI systems and sensitive data
- Identity and access controls
- Backup infrastructure and recovery processes
- Human behavior and automated workflows
At the same time:
- AI introduces new risks like prompt injection, model manipulation, and agent misuse
- Data sprawl makes it harder to control access and prevent exposure
- Ransomware increasingly targets backups and recovery systems first
A fragmented approach creates blind spots.
NRI applies proven red team methodology simulating realistic attack scenarios across both technical and human layers combined with structured testing and planning to deliver a complete view of risk and resilience.
This ensures organizations can not only identify risk but validate and improve their ability to withstand and recover from it.
Benefits
Gain a complete view of AI and data risk
Understand how vulnerabilities across AI systems, data environments, and backup infrastructure connect and create exposure.
Move from reactive security to continuous assurance
Incorporate repeatable testing and planning to continuously validate controls and reduce risk over time.
Strengthen resilience against modern threats
Ensure your organization can detect, respond to, and recover from attacks that target both data and AI systems.
Validate real-world impact
Test how attacks could affect operations, data integrity, and recovery capabilities, not just theoretical risk.
Enable confident innovation and growth
Secure AI adoption and data usage without slowing the business.
What NRI Delivers
AI Red Teaming & Testing
Simulate real-world attacks against AI systems, copilots, and workflows while validating controls, guardrails, and model behavior.
- Prompt injection and jailbreak testing
- Model manipulation and misuse scenarios
- Agent and workflow abuse simulation
- Data exposure and retrieval-layer validation
Backup Security Testing
Ensure your last line of defense holds when it matters most.
- Ransomware resilience testing for backup environments
- Backup integrity and immutability validation
- Recovery time and recovery point objective (RTO/RPO) testing
- Access control and privilege escalation assessment
- Verification of restoration workflows under attack conditions
Data Protection Planning
Design and implement strategies to secure sensitive data across its lifecycle.
- Data classification and risk mapping
- Access control and identity alignment
- Data loss prevention (DLP) strategy
- Governance and policy development
- Alignment to regulatory and business requirements
Integrated Attack Path Simulation
Test how attackers move across AI systems, data environments, and backup infrastructure to expose real-world risk.
Human and Process Risk Testing
Evaluate how user behavior, trust in AI, and operational workflows can be exploited, extending beyond technical controls.
Reporting, Prioritization, and Remediation Guidance
Deliver clear, business-aligned insights with prioritized recommendations and repeatable validation pathways.
How It Works
- Scope AI systems, data environments, and backup architecture
- Conduct structured testing across controls and configurations
- Execute adversary-led scenarios across AI and data layers
- Validate exploitability, impact, and recovery readiness
- Prioritize findings based on business risk
- Align remediation with stakeholders
- Re-test to validate improvement
- Report outcomes and recommendations
Built for Your Entire Technology Ecosystem
NRI’s AI & Data Security integrates across your environment:
- AI platforms (copilots, LLMs, agent-based systems)
- Cloud, SaaS, and hybrid infrastructures
- Data platforms and storage systems
- Backup and recovery solutions
Designed to work with your existing tools without requiring replacement.
Why Organizations Choose NRI
One unified approach to AI and data security
Address risk across AI, data, and backup systems rather than treating them as separate initiatives.
Balanced approach: adversary simulation + structured validation
Combine real-world attack simulation with repeatable testing to ensure both realism and consistency.
Built on proven red team methodology
Leverage approaches that simulate the tactics, techniques, and behaviors of skilled attackers to evaluate real exposure.
Extends beyond technical vulnerabilities
Assess risk across:
- Technology
- Human behavior
- Business processes
Delivering a complete, real-world view of exposure.
Designed for continuous assurance
Move beyond one-time assessments to ongoing validation and measurable improvement.
Ready to secure your AI and data ecosystem?
Identify risk, validate controls, and strengthen resilience with NRI AI and Data Security.
Request an AI & Data Security assessment
FAQ: AI Red Teaming & Testing
What is AI red teaming and testing?
AI red teaming and testing is a comprehensive approach to evaluating AI systems by combining adversary simulation with structured validation. Red teaming mimics real-world attackers to uncover how AI systems could be exploited, while testing systematically evaluates controls, guardrails, and model behavior. Together, they provide a complete view of AI risk across models, data, users, and workflows.
How is AI red teaming different from traditional penetration testing?
Traditional penetration testing focuses on identifying vulnerabilities in applications and infrastructure. AI red teaming goes further by testing model behavior, prompt manipulation, data exposure, and human interaction risks. It evaluates how multiple weaknesses can be combined in realistic attack scenarios to understand true business impact.
What risks does AI red teaming identify?
AI red teaming and testing uncovers both technical and non-technical risks, including:
- Prompt injection and guardrail bypass
- Sensitive data exposure or leakage
- Unsafe or unintended AI actions
- Identity and access control gaps
- Social engineering and misuse of AI outputs
This approach aligns with red team methodology that evaluates real-world attack paths—not just isolated vulnerabilities.
FAQ: Backup Security Testing
Why is backup security testing important?
Backup systems are a primary target in modern ransomware attacks. If backups are compromised, organizations may be unable to recover critical data. Backup security testing ensures that your backup environments, access controls, and recovery processes remain secure and functional under real-world attack conditions.
How do we know if our backups will work during a ransomware attack?
The only way to validate backup readiness is through real-world recovery testing. Backup security testing simulates attack scenarios to confirm:
- Data can be restored successfully
- Recovery times (RTO) meet business expectations
- Recovery points (RPO) align to acceptable data loss thresholds
- Backup integrity has not been compromised
This ensures your organization can recover quickly and confidently during an incident.
What does backup security testing include?
Backup security testing typically includes:
- Validation of backup integrity and immutability
- Ransomware simulation against backup systems
- Testing of restoration workflows and procedures
- Review of access controls and privileged accounts
- Assessment of recovery performance against defined SLAs
The goal is to ensure backups are not just present but secure, accessible, and reliable when needed.
FAQ: Data Protection Planning
What is data protection planning?
Data protection planning is the process of designing strategies, controls, and policies to secure sensitive data across its lifecycle, from creation and storage to access, sharing, and disposal. It ensures that data is protected against unauthorized access, loss, and misuse while supporting business and regulatory requirements.
How do organizations identify and prioritize sensitive data?
Organizations begin by classifying data based on sensitivity and business impact, then mapping where it resides and how it is accessed. This includes:
- Identifying regulated data (e.g., PII, PHI, financial data)
- Understanding data flows across systems and users
- Evaluating who has access and why
This foundation enables organizations to apply the right controls and prioritize risk effectively.
What are the biggest data protection risks today?
Modern data protection risks include:
- Overexposed or misconfigured cloud storage
- Excessive user access and privilege creep
- Data leakage through AI systems and integrations
- Lack of visibility into data movement
- Inadequate controls across SaaS and hybrid environments
A strong data protection strategy addresses these risks holistically across technology, people, and processes.
How does data protection support AI security?
AI systems rely heavily on data, making data protection critical to AI security. Poor data controls can lead to:
- Unauthorized data access through AI workflows
- Leakage of sensitive information via model outputs
- Increased exposure through integrations and retrieval layers
Data protection planning ensures that AI systems operate secure, governed, and well-controlled data, reducing overall risk.
How often should data protection strategies be reviewed?
Data protection strategies should be reviewed regularly and whenever there are major changes, such as:
- New AI initiatives or integrations
- Cloud migrations or new platforms
- Regulatory updates
- Changes in business operations or data usage
Ongoing review ensures controls remain aligned to evolving risk.
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