How Technology Companies Can Securely Adopt AI Agents

Last updated: June 15, 2026

Securely adopting AI agents means giving autonomous software the access it needs to act while keeping that access scoped, inspected, and reversible. Technology companies face this first and hardest: 83% planned agentic deployments, but only 29% felt ready to secure them (Cisco, 2026).

What does securely adopting AI agents mean for technology companies?

In a technology company, an AI agent reads context from an AI model and acts through tools, often touching source code, secrets, and customer data. Securing it means governing two paths at once: the agent-to-model intelligence channel and the agent-to-tools execution channel (Aurascape, 2026).

Technology companies sit on both sides of the agent question. They use agents internally, in engineering, support, and operations, and they build agents into the products customers buy. That dual role doubles the surface to secure. The intelligence channel carries prompts and responses between an agent and its AI model, where prompt injection and data exposure happen. The Tool Execution Channel carries the agent’s actions through tools and the Model Context Protocol (MCP), where an agent can read a repository, query a database, or change code. Governing only one channel leaves the other open.

Why do AI agents pose a higher risk in technology companies?

Technology companies adopt agents the fastest and connect them to the most sensitive systems. Cisco found agents now reach source code repositories, internal databases, and cloud dashboards, where they can open pull requests and modify code (Cisco, 2026). A readership poll ranked agentic AI the top attack vector for 2026 (Dark Reading, 2026).

The crown jewels in a software business are source code, intellectual property (IP), secrets and application programming interface (API) keys, and multi-tenant customer data. Agents reach all of them. An agent connected to a continuous integration and continuous delivery (CI/CD) pipeline can act at machine speed, and a single over-permissioned identity can move data across systems that were never meant to be linked. The same autonomy that makes agents useful is what makes a compromised one costly.

What are the top AI agent security risks for technology companies?

The top risks center on what agents can reach and do: source code and secret exposure, coding agents with write access to production, shadow MCP servers, and over-permissioned machine identities. In one survey, 80% of organizations reported agents taking unintended actions, and 23% said agents were tricked into revealing access credentials (SailPoint, 2025).

Prompt injection sits behind many of these failures. In December 2025, OWASP published its first Top 10 for agentic applications, naming goal hijacking, memory poisoning, and inter-agent communication risks, alongside its established Top 10 for Large Language Model Applications, where prompt injection is the leading entry (OWASP, 2025).

Risk What it looks like in a technology company Why traditional tools miss it
Source code and IP exposure An agent or coding assistant with repository access sends proprietary code to an external model Network tools see encrypted egress, not what the agent sent to the model
Coding agents with write access to production An autonomous agent edits code, runs migrations, or deletes data during a deploy Identity tools authorize the account; they do not judge the action
Shadow MCP servers and tool supply chain Developers connect agents to unvetted MCP servers, and a poisoned tool gains reach Tools are not protocol-aware for MCP and cannot read tool-call intent
Over-permissioned non-human identities Service accounts and agent identities hold broad, standing access to repos, clouds, and databases Legacy identity governance was built for human joiners and leavers, not machine-speed agents
Cross-tenant data leakage in multi-tenant SaaS An agent retrieves or returns one customer’s data inside another customer’s context Application logs may not flag it, and data loss prevention is not watching model context
Prompt injection in product agents A malicious input in user content hijacks an agent embedded in your product Web and application firewalls do not parse model instructions

Which standards and frameworks apply to AI agents in technology companies?

No single law governs AI agents in technology, so teams combine frameworks. The NIST AI Risk Management Framework structures how to manage agent risk (NIST AI RMF, 2024), OWASP names the failure modes such as prompt injection (OWASP, 2025), and AI policy is shifting quickly across major markets in 2026 (Cisco, 2026).

Framework What it addresses Relevance to AI agents in tech
NIST AI Risk Management Framework A voluntary framework to map, measure, and manage AI risk Gives teams a governance structure for agent risk
OWASP Top 10 (LLM and agentic applications) Catalogs prompt injection, insecure output, and agentic threats like goal hijacking Names the specific failure modes agents introduce
SOC 2 and ISO/IEC 27001 Security controls customers and auditors expect from software vendors Agent access to customer data falls inside audit scope
EU AI Act Obligations for providers and deployers of certain AI systems Applies if the company builds or ships AI features
GDPR and CCPA Data protection and privacy obligations Apply whenever agents process personal data

What controls should technology companies put in place to secure AI agents?

Effective programs apply least privilege and fail-safe defaults to every agent, a baseline CISA and international partners recommend for agentic AI (CISA, 2026). Visibility comes first: only 21% of organizations keep a real-time inventory of their agents (CSA, 2026).

Aurascape organizes these controls around three pillars: See, Test, and Protect. You cannot govern what you cannot see, so discovery comes before enforcement.

Control What it does Pillar
Discover every AI agent, including on endpoints Builds a real-time inventory across SaaS, the browser, and developer machines See
Enforce least privilege for non-human identities Scopes agent and service-account access to the minimum and removes standing access Protect
Govern the tool execution channel Inspects and controls every MCP tool call through a gateway Protect
Inspect the intelligence channel Checks prompts and responses for prompt injection and sensitive data Protect
Test agents before production Runs guardrail and prompt-injection tests before deployment Test
Monitor and audit agent actions at runtime Records actions across both channels for investigation and compliance See

How should a technology company start securing AI agents?

Start small and govern before you scale. CISA recommends incremental adoption with fail-safe defaults (CISA, 2026), and Gartner expects more than 40% of agentic AI projects to be cancelled by 2027, often from weak governance (Gartner, 2025).

A practical order works in four moves. First, discover the agents already running across your SaaS apps, browsers, and developer laptops, since shadow adoption is common in engineering teams. Second, assess and test agent behavior against prompt injection and policy before anything reaches production. Third, enforce least privilege and route agent traffic through a gateway and proxy so tool calls and model context are inspected. Fourth, monitor actions at runtime and keep an audit trail. Governance maturity, not model choice, is what separates the deployments that last from the ones that get pulled. Only 44% of organizations report having a company AI policy at all (Vanta, 2026).

How does Aurascape help technology companies securely adopt AI agents?

Aurascape secures both agent channels and discovers agents across the network and on employee endpoints, including agents running locally on developer machines, a gap network-only and identity-only tools miss (Aurascape, 2026). It complements identity governance and code-scanning tools rather than replacing them.

The AI Proxy inspects the intelligence channel for prompt injection and sensitive data such as source code and secrets. The Zero-Bypass MCP Gateway inspects and governs every MCP tool call in the Tool Execution Channel, so an agent cannot reach a tool or server without passing policy. Safe Output Governance applies data controls to agent actions and model context. Aurascape works alongside identity providers like Okta and SailPoint, which authorize who an agent is, and alongside code-scanning tools, which review the security of code. For coding agents specifically, see securing AI coding assistants in technology companies.

Capability Identity-first and network-first tools Aurascape
Discover AI agents across SaaS, browser, and developer machines Partial; identity tools see registered accounts, network tools see sanctioned egress Discovers agents across the network and on employee endpoints, including locally run agents
Govern the tool execution channel (MCP tool calls) Limited; not protocol-aware for MCP Zero-Bypass MCP Gateway inspects and governs every MCP tool call
Inspect the intelligence channel (prompts and responses) Network tools see encrypted traffic, not model intent AI Proxy inspects prompts and responses for prompt injection and sensitive data
Stop source code, secrets, and customer data leaving via agents Data loss prevention is tuned for files and web, not agent tool calls and model context Safe Output Governance applies data controls to agent actions and model context
Pre-deployment guardrail testing of agent behavior Not offered Tests agents against prompt injection and policy before production
Runtime monitoring and audit of agent actions Logs network and identity events with limited action-level context Records agent actions across both channels with a full audit trail

Aurascape requires agent traffic to pass through the AI Proxy, which is how it inspects intent that encrypted network tools cannot read. Book a demo to see agent discovery and governance on your own environment.

Frequently asked questions

What are the top AI agent security risks for technology companies?

Among the top risks are source code and secret exposure, coding agents with write access to production, shadow MCP servers and tool supply-chain compromise, over-permissioned machine identities, and cross-tenant data leakage in multi-tenant SaaS. In one survey, 80% of organizations reported agents taking unintended actions (SailPoint, 2025).

Do AI agents put source code and customer data at risk?

Yes, when agents have repository, database, or cloud access and that access is not scoped or inspected. Cisco reports agents that can query internal databases and modify code (Cisco, 2026). Controls that inspect the model context and every tool call are what reduce that exposure.

Can identity and access tools secure AI agents on their own?

No. Identity tools authorize who an agent is, but they do not read what an agent sends to a model or what it does through a tool. They pair well with agent-aware inspection of the intelligence and execution channels, which is the gap Aurascape fills.

How can a technology company start securing AI agents?

Discover the agents already running across SaaS, browsers, and developer machines, enforce least privilege on agent identities, route traffic through a gateway and proxy, test agents before production, and monitor actions at runtime. CISA recommends incremental adoption with fail-safe defaults (CISA, 2026).

Related reading: How to Securely Adopt AI Agents, the AI security landscape in 2026, what is prompt injection, and AI data leakage.

This page is a side-by-side comparison for informational purposes. Product capabilities reflect Aurascape’s documentation as of the date above and may change.

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