Global businesses are racing to deploy artificial intelligence, sometimes at the expense of their cybersecurity postures.
The rise of “agentic AI” is especially notable, due to its potential to automate business workflows, cloud operations, engineering tasks and cybersecurity. Not only are AI agents getting more capable, but they can also work much faster than humans, without taking breaks or needing days off. Companies can get more work done at lower costs, and in some cases even freeing up humans to focus on more strategic tasks.
One recent report highlights the scale of agentic AI adoption, with 48% of organisations globally now automating some processes, and 79% saying they plan to spend more on AI agents in the next two years.
However, AI agents also present trust and safety challenges. The same report puts a spotlight on concerns enterprise leaders have regarding the reliability and security of agentic systems. Some 80% of respondents said they’re wary about the technology’s lack of transparency. Moreover, 84% said they’re worried about implementing agents in their day-to-day processes due to a lack of security controls.
These fears explain why adoption has been so uneven. While 80% of enterprises are using chatbots to perform relatively simple tasks such as answering customer support questions, a lot less are using them to automate mission-critical workloads.
This makes it clear that for enterprises to reach the next level of automation, they’re going to need to work on areas such as agent orchestration, governance, identity control, visibility and runtime protection.
Treat AI Agents As Privileged Identities
Nowadays, autonomous AI agents can access many of the same systems and tools, and perform similar tasks to people, which means they should be governed in a manner similar to any other identity.
By giving each AI agent a machine identity, organisations can implement least-privilege access and keep a lid on what they can and cannot do, in the same way they restrict what their human employees can access. Instead of giving agents broad permissions, authorisation should be separated across the various tools, APIs and data sources they use in order to prevent them from carrying out unauthorised and potentially damaging actions.
Without these restrictions, AI agents can potentially wreak havoc with enterprise systems, taking critical servers and databases offline and shutting down business operations.
Earlier this year, one CEO learned the hard way after a rogue AI agent ended up deleting his company’s production database, together with all of its backups, in an effort to fix a “credential mismatch.” Its actions brought the entire business screeching to a halt, and taught the CEO a very important lesson; agents should only be given access to the tools and systems they need to carry out their assigned tasks.
Secure the MCP and Tool Access Layer
MCP security is critical for AI agents because it serves as a key enabler, allowing them to interact with third-party tools and systems to get work done. When agents connect to a company’s files and applications, they can potentially delete critical data, make changes to their settings and do other things that introduce problems and security risks for enterprises.
The best way to secure MCP deployments is to validate server integrity using cryptographic signatures and vetted registries. Teams should apply least-privilege access at the tool level using on-behalf-of authentication. Trust boundaries should be established through a centralised gateway, with risky actions such as deleting database records explicitly requiring human approval to prevent accidental deletion or unauthorised changes.
Finally, enterprises must set up a monitoring system to continuously observe agent-to-tool interactions to ensure nothing slips through the net.
Implement Real-Time AI Telemetry and Runtime Monitoring
Traditional system logs don’t cut it for AI agents because they don’t provide visibility into key aspects of autonomous systems, such as prompts, tool calls, reasoning processes, agent-to-agent interactions and task execution.
Enterprises require granular visibility into their agents so they can monitor and understand the decisions and actions they make and ensure they’re kept under control.
By implementing specialised AI telemetry, security operations teams can analyse agentic data to detect anomalous behavior, prompt injection attacks and other forms of abuse in real time and intervene before any damage is done.
Continuously Assess AI Agent Posture
Static security checks are ineffective for AI agents, because their risk profiles evolve dynamically, based on the permissions they have and the “skills” they’re allowed to access. Teams should instead use dedicated AI security posture management tools to address this reality.
By continually assessing the behavior and permissions of agents, teams can detect “drift,” which is when behavior deviates from standard norms. They’ll also be able to identify shadow IT, or the presence of unsanctioned AI agents in an environment that may bypass governance controls.
Finally, these tools enable “overprivileged” agents to be stopped in their tracks before they cause any mischief.
Secure the AI Supply Chain
AI supply chains, which span everything from the LLMs to the orchestration frameworks and third-party SDKs, can be riddled with security risks.
To mitigate these dangers, organisations must maintain an “AI bill of materials” or AI-BOM, essentially, an inventory of each model, library and SDK within their agentic ecosystem. They should scan their agents, models and dependencies regularly to check for malicious code that might be triggered by specific inputs, and also vet orchestration frameworks to ensure the code used to manage agent logic is secure.
By ensuring supply chain visibility, organisations can prevent malicious actors and the agents themselves from leveraging vulnerabilities to start performing unauthorised actions.
Build Guardrails Into the AI Development Lifecycle
Where agents are concerned, security must be integrated within DevOps pipelines from the get-go. Developers should use automated testing to check that their agents’ code meets the organisation’s security standards.
When these guardrails are integrated from the beginning, it’s easier to catch problems before they wind up in production.
Keep Humans In The Loop For High-Risk Actions
Humans must always have the final say before agents carry out critical actions. As such, organisations must define clear escalation paths to maintain control over their agentic fleets.
This involves establishing approval workflows for sensitive tasks like altering financial records or adjusting the settings of a mission-critical app.
Developers must also include a “kill switch” and use rollback mechanisms, so agents can immediately be shackled should erratic behavior be noticed. To maintain accountability, audit trails should be implemented too.
Tools Of The Trade
The rapid adoption of agentic AI has sparked the emergence of a burgeoning ecosystem of AI security tools, including these leaders:
- Wiz is an essential tool for agent developers and orchestration security teams, because it unifies AI security posture management with traditional cloud security workflows. With Wiz, organisations can easily inventory their AI agents, models and exposed endpoints across every environment, understand their behavior and identify risks using a single, comprehensive platform
- Protect AI is all about securing AI supply chains and scanning models for vulnerabilities. By providing full visibility via AI-BOMs, it enables teams to ensure the individual components of their agents are free of vulnerabilities
- Obsidian Security provides capabilities around SaaS and identity management, which can be useful for monitoring how agents interact with apps such as Microsoft 365 and Salesforce. It can help spot agents with risky permissions and identity-based risks within third-party software
- Billed as an AI-native security and governance platform, Zenity provides sophisticated, agent monitoring capabilities designed to keep tabs on both their behavior and interactions. This makes it an ideal platform for implementing guardrails and runtime protections
Agentic orchestration tools have become a critical element of enterprise IT infrastructures. As more workloads are handed off to AI agents, security teams have no choice but to prioritize agentic visibility, MCP security, runtime protection and continuous posture management to prevent risks.
The organisations that adopt these practices first will be in pole position to scale agentic automation in 2026 and beyond. By leveraging integrated security platforms such as Wiz, security teams can quickly adapt to this reality and help their organisations emerge at the forefront of enterprise automation.
