Keeping Security Relevant as AI Reshapes Business Risk

By April Miller

Artificial intelligence (AI) is changing how companies identify talent, write customer service scripts, detect fraud, forecast supply chains, and even strategize in meetings. It also broadens the attack surface in unexpected ways.

Cybersecurity leaders can no longer describe security as a back-office IT concern. Cyber risk travels between departments. So does the risk from AI. If organizations want to stay safe, governance, automation oversight, and communication structures will become key design considerations.

Rapid AI Adoption Expanding Attack Opportunities

Although AI introduces new efficiencies, it also introduces new attack vectors, such as data leakage and model poisoning. In addition, third-party tools can create vulnerabilities that security teams avoid in their own data centers.

According to IBM’s 2024 Cost of a Data Breach Report, the global average cost of a data breach increased to $4.88 million in 2024. The rise in expenses is due to increasingly complex environments, many of which are based on automated workflows and AI-driven decisions.

AI is also connected to customer records and to financial and operational systems. An attack that compromises these can affect the technology as well as regulatory compliance, brand image and enterprise strategy.

Security Now Spans Every Department

AI reduces the divide between cybersecurity and the rest of the organization, with marketing teams using generative AI to create content and finance departments using predictive models. HR professionals also use screening algorithms, which produce data governance responsibilities.

Additionally, leaders need to take a cross-departmental, cross-functional approach to AI security that goes beyond network monitoring and endpoint controls. Members of legal, compliance, data science and operations should work together to set policies and ensure AI is used in a compliant manner that aligns with the enterprise’s risk tolerance.

Without cross-functional collaboration, shadow AI will likely proliferate. Employees use public tools that may process proprietary data outside of sanctioned systems. Clear policies, upskilling employees and centralized visibility can reduce these risks while maintaining the pace of innovation.

Better Safeguards for Automated Systems

Most companies have some form of control and intrusion detection in place. Still, tech support must also validate AI systems, test data integrity, and monitor system and model behavior to protect against attacks.

Today’s attacks by adversaries are increasingly automated. Phishing campaigns are also more advanced. Attackers will target the pipeline logic rather than the architecture. As offensive tactics increase, security must increase proportionally.

Incorporating security in AI development workflows increases resilience. Threat testing during AI model design reduces the cost of remediating vulnerabilities. Logging model interactions and storing auditable training datasets can improve transparency and ease incident response.

Examples of quantifiable risk include sensitive data exposure events, vendor audit results and anomaly detection accuracy. Since decisions often focus on quantified risk, metrics should make technical risk meaningful to leadership.

Communication Infrastructure as a Security Control

System availability and contingency communications are core aspects of cybersecurity resilience planning. To keep security up to date, IT professionals must preserve access to alternative communication channels that operate independently of the primary capabilities of digital networks.

Organizations that manage or host large events, operate dispersed workforces, or work in emergency response often use radio systems as part of their contingency plans. For example, companies maintain critical communications for teamwork when primary networks experience congestion or outage. Independent communication tools like radio allow continuity of command in a pressured infrastructure situation.

When disruptions occur in the AI code, decisions about how to respond will likely need to be made at every level, from leadership to departments, using cloud-based tools to coordinate and communicate during the event. A rapid response to security events improves the outcome.

Security strategy also concerns communication resiliency, endpoint defense and monitoring security. When senior leadership retains clear authority and an open information flow, disruption containment proceeds more quickly and reduces the impact on reputation.

Cybersecurity Relevance in the AI Era

The importance placed on security will depend on the business purpose. Decision-makers will need to be prepared to treat AI risk as an executive governance matter, not a technical one. It starts with structured assessment and straightforward reporting.

A focused framework may include:

• Executive AI risk reviews tied to enterprise performance indicators
• Monitoring model performance and data drift
• Independent assessments of third-party AI vendors
• Zero-trust architecture with data divided into isolated segments

AI governance and the hierarchical reporting structure embed accountability from the outset, ensuring security evolves at the same pace as development and innovation. Other industry examples of how resilience and automation are linked include AI search tools used for information retrieval and enterprise risk visibility.

Intelligent Security Growth

AI expands opportunity, but without structure, it magnifies exposure. Security matters when leadership extends oversight beyond IT, modernizes defenses for automated systems, and strengthens resilience in communication and critical infrastructure. Organizations that implement disciplined, mindful AI risk metrics and governance aligned with their overall strategy will be best prepared over the long term. The time to act is now.

April Miller is a Senior Writer at ReHack. She has more than 5 years of experience writing on cybersecurity. You can explore more of her work at ReHack.com or connect with her on LinkedIn.

Photo by Hitarth Jadhav: https://www.pexels.com/photo/close-up-photo-of-keyboard-220357/