Artificial Intelligence (AI) continues to reshape cybersecurity, offering immense potential to strengthen defense systems and ease the workload of security professionals. From reducing alert fatigue to identifying complex attack patterns at scale, AI can help defenders work smarter and faster.
However, to truly benefit from AI, organizations must also secure the very systems that enable it. Without strong governance, identity controls, and visibility into AI decision-making, even the most well-intentioned deployments can introduce new risks faster than they mitigate them.
To unlock AI’s full promise safely, defenders need to approach AI security with the same rigor they apply to any other mission-critical infrastructure — establishing trust, accountability, and oversight across every layer.
Establishing Trust in Agentic AI Systems
As enterprises integrate AI deeper into their security workflows, identity security becomes the bedrock of trust.
Every AI model, script, or autonomous agent now functions as a new identity — one capable of accessing sensitive data, issuing commands, and influencing outcomes. If not properly governed, these identities can quietly turn from defensive assets into potential liabilities.
This is especially true for Agentic AI systems — those capable of making and executing decisions without human intervention. These AI agents might triage alerts, enrich threat data, or even trigger incident response playbooks automatically. Each of these actions represents a transaction of trust that must be authenticated, authorized, and auditable.
To build this trust foundation, the same security principles applied to people and services must now apply to AI:
- Scoped credentials & least privilege: Limit every model or agent’s access to only what’s required.
- Strong authentication & key rotation: Prevent impersonation or credential leakage.
- Provenance & audit logging: Ensure every AI-driven action can be traced and reversed if needed.
- Segmentation & isolation: Contain potential compromise and prevent cross-agent interference.
In short, treat every AI system as a first-class identity within your Identity and Access Management (IAM) framework. Assign ownership, define lifecycle policies, and continuously validate not just what the AI was designed to do — but what it’s actually capable of doing.
Securing AI: Best Practices That Work
Securing AI isn’t only about defending data — it’s about protecting the entire AI ecosystem, including models, pipelines, and integrations. These components should be treated as mission-critical assets requiring layered and continuous protection.
The SANS Secure AI Blueprint provides a solid starting point through its Protect AI track, outlining six core control domains derived from the SANS Critical AI Security Guidelines:
- Access Controls: Enforce least privilege, multi-factor authentication, and continuous access monitoring for all AI components.
- Data Controls: Validate and sanitize all data used for training and inference to prevent model poisoning and data leakage.
- Deployment Strategies: Harden AI pipelines using sandboxing, CI/CD gating, and pre-release red-teaming.
- Inference Security: Guard against prompt injection and misuse by applying input/output validation and escalation paths.
- Monitoring: Continuously track model behavior for drift, anomalies, or compromise indicators.
- Model Security: Version, sign, and verify models throughout their lifecycle to prevent tampering or unauthorized retraining.
These practices align directly with the NIST AI Risk Management Framework and the OWASP Top 10 for LLMs, helping teams turn theoretical guidance into practical defense mechanisms. Once these fundamentals are established, security teams can make informed decisions about when to trust automation — and when to keep humans in the loop.
Balancing Automation and Human Oversight
AI can act like a tireless digital intern — processing vast amounts of data and spotting anomalies faster than any human. But not every task should be fully automated.
Security teams need to distinguish what to automate from what to augment:
- Automate: Tasks that are repetitive, low-risk, and data-driven — such as threat enrichment, log parsing, and alert deduplication.
- Augment: Decisions that require human judgment, ethics, or contextual understanding — such as incident scoping, attribution, and response prioritization.
Finding this balance depends on each organization’s risk tolerance and operational maturity. When the cost of an automation error is high, keep humans involved. When the outcome is predictable and measurable, let AI take the wheel.
Looking Ahead — Secure AI in Practice
AI’s potential in cybersecurity is immense, but so are the challenges. To ensure AI remains an ally rather than a vulnerability, defenders must prioritize trust, transparency, and accountability in every AI-driven process.
Want to dive deeper? Join the discussion at SANS Surge 2026 (Feb 23–28, 2026), where experts will explore how to build AI systems that are not only powerful but also safe to depend on.
As AI becomes central to security operations, its security must become central to ours.