• New Tines report finds AI adoption in security rising, but automation remains blocked by integration failures and restrictive policies.
  • Integration gaps — legacy tools, API mismatches and vendor silos — force teams back into manual work.
  • Policy and governance hurdles prevent safe automation rollouts, slowing incident response and increasing risk.

What the report found

The recent Tines report (summarized here) highlights a clear mismatch: organizations are experimenting with AI and automation, yet many security operations remain largely manual. According to the report’s central finding, technical integration failures and internal policy barriers are the primary reasons teams can’t turn AI capabilities into automated, repeatable workflows.

Why integration gaps matter

Integration failures are not just a technical annoyance — they prevent playbooks and AI models from acting across the security stack. Common causes include legacy systems without modern APIs, vendor tools that don’t interoperate cleanly, and brittle point-to-point integrations that break under normal change cycles. When tools can’t talk to each other reliably, automation stalls and analysts fall back to manual handoffs.

Policy and governance as a brake on automation

Beyond technical problems, the report points to policy constraints that limit automation adoption. Strict change control, concerns about automated actions touching sensitive data, and conservative approval processes can all block safe automation in production. These governance decisions may be prudent, but without updated policies and clear guardrails they also keep teams from realizing AI’s efficiency gains.

Impact on security teams and operations

The combined effect is predictable: slower incident response, inconsistent investigations, and more repetitive work for analysts. Teams attempting to pilot automation often find themselves spending more time fixing integrations and navigating approvals than improving detection and response. That dynamic reduces the ROI of automation initiatives and slows broader adoption.

Practical steps security leaders can take

  • Prioritize integration-first projects: treat interoperability as a success criterion when procuring tools.
  • Use automation platforms or orchestration layers that abstract API differences and reduce brittle point-to-point scripts.
  • Update policies and governance: create safe, auditable guardrails that enable approved automated actions instead of blanket bans.
  • Start with small, high-value pilots that demonstrate measurable time savings and reduced human error.
  • Partner with vendors or integrators that commit to long-term API stability and collaborative support.

Why this still matters

AI and automation promise real improvements for security operations, but the promise remains conditional. The Tines report shows that without fixing integration and policy roadblocks, organizations will continue to miss efficiency gains — and adversaries that automate will gain an edge. Security teams that treat interoperability and governance as strategic priorities are more likely to convert AI experiments into operational resilience.

Security leaders don’t need to wait for a perfect stack — they need a realistic plan: consolidate where possible, add an orchestration layer, and rewrite policies to permit safe automation. Closing these gaps is the practical path from manual toil to the faster, smarter security operations organizations expect from AI.

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