KEY 4: SECURITY
Fortify the layer
Every leap forward in capability expands the attack surface beneath it.
True AI readiness demands defenses built into the walls, not patched on after the breach. Your most valuable data must remain protected, resilient, and ready for scale.
Not to mention that the global average cost of a data breach reached USD 4.88 million in 2024, up 10% year-over-year.
Signs of your experience level
Beginner
Security is reactive. Basic hygiene may be in place—antivirus, access controls, and perhaps MFA—but there’s little visibility or coordination. Risk assessments happen after the fact, and shadow IT is common. Breaches and compliance failures are constant concerns, with no unified strategy to address them. Security efforts are fragmented, leaving the organization vulnerable.
Intermediate
Security controls are more established, often driven by regulatory requirements. Cloud security posture is improving, but gaps remain between teams, tools, and policies. AI risks, such as data misuse or model vulnerabilities, are not yet addressed. Security is still seen as IT’s job rather than a shared responsibility, limiting its effectiveness across the organization.
Advanced
Security is proactive and integrated into operations. Threat detection and response systems are in place, but AI-specific risks—like data poisoning, IP leakage, or prompt injection—are not fully addressed. Policies exist but often struggle to keep pace with rapid innovation. While progress is evident, gaps in AI risk management create challenges.
Expert
Security is embedded into design and operations. Controls are automated, AI-aware, and enforced across all environments. Risk is managed at the intersection of technology, talent, and governance. The organization defends not just infrastructure but also data pipelines, models, and algorithms. Trust is built into every layer, ensuring resilience and confidence.
Pitfalls
- Treating security as a checklist instead of an enabler
- Assigning security responsibility to IT only, with no business accountability
- Lagging visibility across multi-cloud and hybrid environments
- Ignoring the unique risks introduced by AI and machine learning models
Level requirements
You can’t scale what you can’t secure. Breaches, compliance failures, and IP theft derail AI before it begins.
- A zero-trust architecture with continuous verification of users, devices, and workloads
- Automated detection and response across cloud, data, and application layers
- AI-specific controls such as prompt validation, model audit trails, and data lineage tracking
- A shared culture of accountability across technology, business, and compliance teams
Power up: Security stress test
Level up your resilience with proactive pressure—before reality applies it for you.
A security stress test puts your environment through its paces before an actual breach, audit, or AI rollout does. By simulating targeted risks, from data leakage and access exploits to AI-specific vulnerabilities, you can evaluate how your systems respond. You get clear insight into gaps, misalignments, and blind spots across cloud, platform, and process layers. More importantly, you walk away with a prioritized action plan to harden your environment without slowing innovation. It’s not just a test. It’s how you build the confidence to scale securely.
Confidence unlocked. Your defenses are built in, not bolted on.