12 Must-Ask Questions When Selecting an AI Security Provider
Jay Cuthrell
Chief Product Officer
Artificial intelligence (AI) is reshaping business operations with speed, intelligence, and efficiency. However, as an IT leader, you understand that with innovation comes risk. From compliance gaps to unauthorized access and evolving cyber threats, AI introduces new challenges that demand a proactive, adaptive IT security strategy. Without one, businesses aren’t just vulnerable to breaches—they face rising operational and financial costs.
Organizations using AI-driven security and automation reduce breach-related costs by an average of $2.22 million.1 Meanwhile, the average cost of a data breach surged to $4.88 million in 2024, a 10 percent increase from the previous year.2 Today, AI security isn’t a precaution—it’s a financial imperative. A reliable IT provider goes beyond quick fixes, taking a proactive, long-term approach to AI security. The right partner ensures continuous assessment, helps maintain compliance, and defense against evolving threats—prioritizing resilience over risk.
Is Your AI Security Provider Up to the Challenge?
To choose the right AI security provider, ask these 12 essential questions to confirm they have the expertise and vision to keep your enterprise secure:
1. Policy and Compliance Readiness:
Can you enforce AI security policies as they evolve with the threat landscape?
Regulations such as GDPR, HIPAA, SOC 2, and ISO 27001 are essential to AI security. As AI evolves, so do compliance challenges. A security-first provider anticipates these changes, updates policies, closes security gaps, and ensures ongoing compliance.
Put them to the test:
- Compliance without compromise – Implements and enforces AI-specific security policies, standards, and procedures aligned with regulatory frameworks
- Audit-ready at all times – Conducts regular compliance audits, regulatory assessments, and maintains security certifications for continuous adherence
- Proactive risk mitigation – Identifies gaps in AI security policies and updates standards to mitigate risks before they become regulatory liabilities
- Global data governance – Manages cross-border data privacy laws, ensuring AI integrity and compliance with jurisdictional requirements
- Flexible security strategies – Continuously adapts AI governance frameworks to meet evolving legal, ethical, and security challenges
2. Risk Management:
How can you anticipate and mitigate AI-specific threats before they escalate?
AI security is about staying ahead of threats, not just reacting to them. Risks including adversarial attacks and model exploits require a provider with a proactive strategy—mapping risks, simulating attacks, and continuously refining defenses to prevent breaches.
Put them to the test:
- AI threat modeling expertise – Evaluates data flows, system boundaries, and attack surfaces to identify and predict vulnerabilities
- No blind spots – Conducts comprehensive AI security risk assessments to uncover hidden threats and security gaps while implementing mitigation strategies
- Always watching – Deploys real-time monitoring and automated threat detection to flag anomalies before they escalate
- Understands AI’s weak points – Assesses the criticality, interdependencies, and potential impact of AI-specific risks
- Evolves with the threat landscape – Continuously updates risk models and security controls to counter new AI-driven attack methods
3. Data Protection:
How do you safeguard AI data from breaches, manipulation, and misuse?
AI systems handle sensitive data, making encryption, integrity controls, and governance essential. Without strong safeguards, attackers can exploit vulnerabilities, leading to biased or malicious outputs. A trusted IT team ensures data confidentiality, integrity, and availability while actively detecting and mitigating threats.
Put them to the test:
- End-to-end encryption – Protects AI training data, models, and source code with secure encryption and anonymization at rest and in transit
- Data integrity controls – Implements bias detection, validation, and access restrictions to prevent model manipulation and unauthorized modifications
- Tamper-proof defenses – Hardens AI pipelines against data poisoning, adversarial attacks, and unauthorized data injections
- Continuous monitoring – Deploys real-time logging, anomaly detection, and threat intelligence to flag and contain suspicious activities before they escalate
- AI-specific risk mitigation – Continuously updates data security policies, access controls, and AI model protection to stay ahead of evolving cyber threats
4. Access Control:
How can you prevent unauthorized access and insider threats?
AI models and datasets are high-value cyber targets, making access control a critical security layer. Without strict enforcement, weak access policies can lead to data leaks, model tampering, unauthorized modifications, and regulatory non-compliance. A security-first provider ensures that only verified users, systems, and processes can interact with AI models and datasets—at the right time, with the right level of access, and for the right reasons.
Put them to the test:
- Zero-trust enforcement – Implements least privilege access (LPA), role-based access control (RBAC), and multi-factor authentication (MFA) to prevent unauthorized use
- Continuous access monitoring – Uses real-time anomaly detection to flag and respond to unauthorized logins, privilege escalations, and access abuse
- Secure API and third-party access – Locks down external integrations, AI model endpoints, and API connections to prevent exploitation and data siphoning
- Dynamic privilege management – Regularly reviews, updates, and revokes unnecessary access rights to prevent stale permissions and security gaps
- Insider threat detection – Identifies and prevents unauthorized modifications to AI models, datasets, training pipelines, and system configurations
5. Endpoint Security:
How do you defend AI-powered systems from exploitation?
AI security is only as strong as its weakest endpoint. APIs, IoT devices, and AI applications create attack surfaces cybercriminals can exploit. A security-led provider hardens endpoints, enforces strict access, and continuously monitors for threats to prevent breaches.
Put them to the test:
- AI-aware endpoint protection – Embeds AI-specific threat detection into endpoint security to detect and mitigate targeted attacks
- No backdoors – Implements strict access controls to prevent unauthorized modifications to AI models and APIs
- Real-time threat response – Continuously monitors AI endpoints for misuse, deploying automated defenses against suspicious activity
- IoT and API lockdown – Secures AI-powered IoT devices, cloud interfaces, and third-party integrations from exploitation
- Proactive endpoint threat hunting – Continuously analyzes AI endpoints for emerging attack patterns, adapting defenses in real time
6. Infrastructure Security:
How well can you protect the foundation of our AI environment?
AI security starts with a strong foundation. Unsecured cloud, network, or compute resources create entry points for attackers. As AI, ML, and edge computing drive innovation, securing the infrastructure that supports them is critical to preventing new risks.3 A trusted service provider enforces strict controls, monitors threats, and conducts regular penetration testing to prevent breaches.
Put them to the test:
- End-to-end protection – Implements strict security controls across AI cloud environments, on-premises servers, and hybrid infrastructures
- Penetration-tested defenses – Conducts regular AI-specific penetration testing and vulnerability assessments to expose weaknesses
- Continuous threat visibility – Deploys real-time monitoring, anomaly detection, and automated threat response to protect infrastructure
- Access locked down – Enforces zero-trust architecture, LPA, and network segmentation to restrict unauthorized entry
- Resilient and responsive – Delivers rapid detection, containment, and remediation of infrastructure-based threats
7. Data Governance and Integrity:
How can you ensure that AI data remains accurate, secure, and compliant?
AI is only as reliable as its data. Compromised or biased data can cause flawed decisions, compliance issues, and security risks. An AI-savvy partner enforces governance, validates integrity in real time, and restricts unauthorized modifications.
Put them to the test:
- Governance with accountability – Establishes clear roles and responsibilities for AI data security, access, and compliance
- Integrity from ingestion to output – Conducts continuous data quality assessments, validation, and anomaly detection to prevent corruption
- Tamper-proof safeguards – Enforces identity and access management (IAM) to block unauthorized modifications and protect against inference attacks
- Data use policies that hold up – Implements AI-specific data governance frameworks to align with GDPR, HIPAA, SOC 2, and industry regulations
- Proactive monitoring – Tracks AI data integrity in real time, ensuring no unauthorized alterations or biases impact AI-driven decisions
8. Continuous Security Maintenance:
How do you keep AI security ahead of evolving threats?
AI security isn’t a set-it-and-forget-it process—attack methods evolve, vulnerabilities emerge, and adversarial AI techniques advance. An IT provider with AI expertise takes a continuous, proactive approach, stress-testing AI defenses, applying real-time security updates, and adapting to new threats before they escalate.
Put them to the test:
- No outdated defenses – Enforces automated patching, AI model updates, and security fixes to eliminate known vulnerabilities before exploitation
- Always scanning for trouble – Conducts ongoing AI-specific vulnerability assessments and red team security audits to detect and mitigate weaknesses early
- Under constant attack (by design) – Performs regular AI penetration testing, simulating adversarial attacks and real-world cyber threats
- Resilient, not reactive – Continuously adapts security frameworks to counter emerging AI-driven attack methods and evolving adversarial tactics
- Automated security evolution – Leverages AI-powered monitoring, behavioral anomaly detection, and threat intelligence to update protections in real time
9. Incident Response:
How do you detect, contain, and recover from AI security breaches?
No system is breach-proof, but response speed matters. AI-driven threats demand AI-driven defenses—real-time detection, automated containment, and adaptive remediation. A valued AI security vendor neutralizes attacks fast, minimizes damage, and strengthens future defenses.
Put them to the test:
- Prepared for the Worst – Maintains a documented AI security incident response plan covering threats such as AI-driven phishing, API abuse, and adversarial AI attacks
- Rapid reaction force – Deploys automated threat detection, AI-driven containment, and mitigation protocols to neutralize attacks before they escalate
- Forensic mindset – Uses AI-powered forensic analysis to trace breach origins, understand attack vectors, and prevent repeat incidents
- Always improving – Learns from every incident, updating AI security frameworks and response playbooks to harden defenses against future threats
- Seamless recovery – Restores AI systems, safeguards data integrity, and ensures business continuity through rapid remediation and automated rollback mechanisms
10. AI Supply Chain Security:
How can you secure the AI supply chain from hidden vulnerabilities?
AI systems depend on a complex chain of third-party models, data sources, software libraries, cloud platforms, and hardware. Your AI infrastructure is at risk if any component is compromised. A security-focused IT partner ensures supply chain integrity by vetting, monitoring, and securing every external dependency before integration.
Put them to the test:
- Trusted sources only – Validates and secures AI models, datasets, and third-party tools before integration
- No weak links – Conducts regular security assessments and penetration testing on all AI supply chain components
- Tamper-proof AI – Implements cryptographic integrity checks and access controls to prevent breaches and unauthorized modifications
- Full visibility – Monitors third-party AI dependencies in real time, flagging anomalies before they become threats
- Hardened integrations – Secures APIs, AI-powered SaaS tools, and external AI pipelines
11. Security Training:
How do you equip our teams to defend against AI-driven threats?
AI security is only as strong as the people behind it. Even the most advanced defenses can’t stop threats that employees don’t recognize. AI-powered phishing, deepfakes, and adversarial attacks exploit human vulnerabilities, making comprehensive AI security training essential. As AI continues to evolve, organizations must train teams now to stay ahead, or risk falling behind in an increasingly AI-driven threat landscape.4 Expertise is needed to build a proactive, AI-aware security culture—ensuring teams can detect, respond to, and neutralize threats in real time.
Put them to the test:
- Training that matters – Delivers ongoing, AI-specific security training tailored for IT teams, developers, and business leaders
- Cyber-savvy teams – Educates employees on AI-generated phishing, deepfakes, model inversion attacks, and adversarial AI manipulation
- Proactive defense mindset – Ensures staff can identify, report, and mitigate AI-driven threats before damage occurs
- Security culture at every level – Establishes clear internal policies, role-based security protocols, and AI-specific best practices
- Always evolving – Regularly updates training programs to address emerging AI risks, attack techniques, and evolving threat landscapes
12. Scalability and Future-Proofing:
How do you scale AI security as threats and technologies evolve?
AI security isn’t just about today—it’s about what’s next. As AI models grow more complex, interconnected, and autonomous, security strategies must scale to keep pace with new risks, regulatory shifts, and evolving attack vectors. An AI-ready partner doesn’t just react—they innovate, ensuring your AI security infrastructure is resilient, adaptable, and built for growth.
Put them to the test:
- Built for growth – Delivers scalable AI security solutions that evolve alongside your business, infrastructure, and expanding AI capabilities
- Ahead of the curve – Continuously monitors AI-driven cyber threats, emerging vulnerabilities, and adversarial attack techniques
- Regulatory-ready compliance – Proactively aligns security policies with evolving governance, data privacy laws, and industry regulations
- AI security that evolves – Deploys adaptive defenses that update in real time to counter new AI-based exploits
- Strategic security planning – Partners with your organization to develop a long-term AI security roadmap that scales with your future needs
Additional Resources
As a reminder,5 knowing the risks of AI is crucial for everyone. Here are some resources to help us better understand these risks:
- AI Risk Atlas:6 A free documentation resource to help understand risks associated with AI systems and how to mitigate them.
- ATLAS Matrix:7 A free knowledge base of real-world attack observations in AI systems.
- AI Risk Repository:8 A free database of AI risks organized by cause and domain.
- AI Risk Management Framework:9 A free resource to help organizations identify AI risks and determine suitable actions.
Strengthen Your AI Security with the Right Provider
AI security isn’t a one-time fix—it requires ongoing defense, compliance, and adaptation. The right IT partner doesn’t just check the security boxes—they lead the charge, staying ahead of threats, protecting your data, and ensuring your AI infrastructure remains resilient. Security demands an integrated, long-term strategy that evolves with technology and risk.
NexusTek specializes in enterprise-grade AI security solutions and managed services, helping organizations build resilient defenses that minimize risk and maximize operational efficiency.
Talk AI Security
☑️. Schedule a consultation with our AI security experts
☑️. Get a free security assessment to uncover vulnerabilities
☑️. Learn how NexusTek can help you build a scalable, future-proof AI security strategy
Reference
IBM Security, Cost of a Data Breach Report 2024, July 2024.
2. Ibid.
3. IDC, Worldwide Spending on Public Cloud Services is Forecast to Double Between 2024 and 2028, According to New IDC Spending Guide, July 2024.
4. McKinsey, Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential, January 2025.
5. NexusTek, All Things Open: RTP AI Meeting Recap, February 2025.
6. IBM watsonx, AI Risk Atlas, February 2025.
7. MITRE ATLAS, ATLAS Matrix, Accessed February 2025.
8. MIT, AI Risk Repository, Accessed February 2025.
9. NIST, AI Risk Management Framework, July 2024.
About the Author
Jay Cuthrell
Chief Product Officer, NexusTek
Jay Cuthrell is a seasoned technology executive with extensive experience in driving innovation in IT, hybrid cloud, and multicloud solutions. As Chief Product Officer at NexusTek, he leads efforts in product strategy and marketing, building on a career that includes key leadership roles at IBM, Dell Technologies, and Faction, where he advanced AI/ML, platform engineering, and enterprise data services.