For too long, security teams have been positioned as the gatekeepers who assess what everyone else builds — always evaluating risk, rarely driving innovation. This reactive stance has consequences: When security is seen as a blocker rather than an enabler, businesses find workarounds, and critical controls are implemented too late, if at all. The question is, ‘Will AI be different?‘
The cloud computing lesson we can’t afford to repeat
Security professionals have seen this movie before. When cloud computing emerged, many security teams were skeptical — and sometimes with good reason. But organizations whose security teams adopted cloud early could shape governance practices from the ground up. They understood the technology deeply enough to design effective controls and earned a seat at the table for strategic decisions.
Late adopters, however, found themselves scrambling to secure environments already built without their input. They inherited architectural decisions they had no hand in making and spent years playing catch-up.
With AI, security teams have a rare second chance to get ahead of the curve instead of trailing behind it. But reading white papers isn’t enough. Security teams need hands-on experience with AI to grasp its capabilities, limitations, and unique risks. Without that practical knowledge, controls may be too permissive (creating vulnerabilities) or even too restrictive.
And yes, “too restrictive” is a thing. There’s a temptation to approach AI with extreme caution — to block, restrict, and wait until it’s “more mature.” But this mindset carries its own risk. When security teams become impediments, users don’t simply stop; they route around the obstacles. Shadow IT emerges. Unapproved tools proliferate. And when that happens, security teams lose visibility and control entirely.
The safest path forward isn’t to resist AI — it’s to engage with it proactively and help your organization adopt it responsibly.
Key security focus areas for AI adoption
To lead effectively, security teams need to develop expertise in six AI-specific security domains.
1. Vendor management
Select AI vendors who demonstrate security maturity. Look for proper certifications, transparent security practices, and clear data handling policies. Don’t assume all AI providers are created equal.
2. Data classification and guardrails
Ensure appropriate data is processed by AI systems with robust guardrails against accidental exposure or data leaks. Implement controls that prevent sensitive information from being inadvertently shared with AI models.
3. Prompt injection protection
As AI systems become more integrated into workflows, prompt injection attacks (where LLMs are tricked into following commands by bad actors) pose a real threat. Build defenses against malicious inputs designed to manipulate AI behavior.
4. Access controls for AI workloads
Traditional access control models may not fit AI use cases. Design permissions systems appropriate for how AI agents interact with data and systems.
5. AI agent lifecycle management
Establish clear processes for how AI agents are created, deployed, monitored, and retired. At Miro, we’ve developed an AI Agent Lifecycle Management process that treats AI agents as first-class assets requiring governance throughout their operational life.
6. Governance and continuous oversight
AI systems evolve. Implement monitoring, logging, and regular audits to ensure controls remain effective as models change and new capabilities emerge.
Practical first steps for security teams
Ready to start? Here are concrete ways security teams can begin engaging with AI.
Maintain living policy documents: Use AI to keep security policies current instead of letting them become outdated artifacts. AI can help track changes, suggest updates, and ensure documentation reflects actual practices.
Enhance risk management workshops: Leverage AI to facilitate more effective threat modeling sessions and security reviews. AI can help identify blind spots and generate scenarios you might not have considered.
Shift left with AI driven threat modelling: We have greatly accelerated and improved threat modelling amongst engineering teams adopting AI tools to help them accomplish this at early stages of product development. This does not only improve developers but enables AI code generation with improved quality and security.
Deploy AI security tools: Adopt AI-powered tools for threat detection, incident response, and secure coding practices. Let AI handle pattern recognition at scale while your team focuses on strategic decisions.
Address complex dependencies: Use AI to analyze and manage security vulnerabilities in complex dependency chains — a problem that’s increasingly difficult to handle manually.
Analyze security data: AI excels at processing large datasets. Apply it to your security logs, vulnerability assessments, and threat intelligence feeds to surface insights buried in the noise.
The path forward
The specific AI use cases your security team pursues matter less than the shift in mindset they represent. By actively engaging with AI rather than merely reacting to it, security teams can transform their role from gatekeepers to strategic partners.
This transformation earns you early inclusion in business initiatives. When security teams demonstrate AI fluency and show they can enable innovation safely, they stop being viewed as obstacles and start being seen as essential guides.
The choice is clear: Lead AI adoption in your organization, or spend the next five years reacting to decisions made without you. Choose wisely.
