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Businesses do not need to prepare for a science-fiction takeover to take AI-related cyber risk seriously. Generative AI can help attackers scale familiar tactics such as phishing and malware, while AI applications and agents create new targets of their own. A practical defense starts with ordinary security fundamentals, adds controls for AI-specific risks, and uses specialized tools only where they fit the organization’s systems and needs.
What does the AI threat to businesses actually look like?
NIST describes two linked information-security concerns: generative AI may lower barriers to offensive cyber activity, and AI systems themselves add an attack surface. That is a grounded risk picture—not evidence that a Skynet-like event is likely or imminent. The available sources do not quantify the probability of such an event or provide a single figure for the business impact of AI-driven attacks.
AI can assist with familiar threats, including hacking, malware, and phishing. At the same time, attackers may target an AI system’s availability, or try to undermine the integrity or confidentiality of its code, training data, and model weights. Prompt injection and data poisoning are among the vulnerabilities NIST identifies.
How can attackers exploit an AI application?
Direct and indirect prompt injection
Prompt injection occurs when malicious instructions influence an AI system’s behavior. The instructions do not have to come from a user’s message: an attacker may place them in a document, webpage, or other material that an AI application retrieves and processes. This indirect route matters when an application connects a language model to company data or tools.
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NIST reports demonstrations against connected large language model applications in which attacks could steal proprietary data or run malicious code remotely. These examples show why a connected agent should not receive broad access simply because it can use tools. Limit the data and actions it can reach, and keep consequential operations subject to appropriate oversight.
Data poisoning and other system risks
Data poisoning can compromise the data used to train or operate an AI system. More broadly, organizations need to consider whether an AI service can be disrupted and whether its code, data, or model weights can be exposed or altered. The relevant controls depend on how the system is built, where it runs, and what it can access.
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What should a business do before deploying AI agents?
CISA and international partners’ May 1, 2026 guidance recommends careful adoption of agentic AI. Translate that advice into concrete security work before and after deployment:
- Align AI risk with existing cybersecurity governance. Use the organization’s security framework and risk processes to account for AI systems and agents.
- Scope permissions tightly. Give an agent only the identity, data, and tool access it needs for its assigned task. Avoid broad access to sensitive systems.
- Use layered defenses and strong identity management. Do not rely on a single AI-specific control to compensate for weak access management or other security gaps.
- Threat-model before deployment. Identify what the agent can read, change, or trigger, and consider how malicious inputs or compromised connected services could affect it.
- Monitor use continuously. Watch AI activity and connected systems for unexpected access or actions.
- Assess controls regularly. Revisit permissions, threat assumptions, and safeguards as agents, integrations, and business use change.
NIST’s AI Risk Management Framework (AI RMF) offers voluntary guidance for managing AI risks across design, development, use, and evaluation. Its Generative AI Profile was released in July 2024. NIST says AI RMF 1.0 is being revised, so organizations should check the current framework status when adopting it.
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Which AI security tools are available?
Vendor documentation now describes products for AI workload discovery, posture management, testing, and runtime controls. The examples below are vendor-described capabilities, not independent comparative test results or guarantees that a product will prevent attacks. A framework or internal policy helps govern risk; it is not a substitute for runtime security controls.
| Product | Vendor-described capabilities | Potential fit and points to verify |
|---|---|---|
| Microsoft Defender for Cloud AI security posture management | Microsoft describes discovery of AI workloads and an AI bill of materials, posture recommendations, attack-path analysis, and discovery across Azure OpenAI, Azure AI Foundry, Azure Machine Learning, Amazon Bedrock, and Google Vertex AI. | Consider it if cloud AI inventory and posture management are priorities. Microsoft documentation says agent-level discovery and posture for Foundry and third-party cloud agents require Microsoft Agent 365 effective July 1, 2026. Confirm current coverage and licensing for the specific environment. |
| Microsoft Defender for Endpoint AI agent runtime protection | Microsoft describes inspection of user prompts, agent tool calls, and tool responses for supported local agents, with audit and block modes. | Relevant where local agents can read files or invoke tools. The feature is marked Preview in Microsoft documentation last updated September 16, 2026. Microsoft recommends starting in audit mode to validate detections before enabling blocking. Check the current supported-agent list and licensing. |
| Cisco AI Defense | Cisco describes AI asset discovery, supply-chain scanning, algorithmic testing and red teaming, plus runtime protections for prompts, responses, and agent/MCP interactions. | Compare its discovery coverage, testing workflow, runtime enforcement, deployment fit, and data handling with your requirements. These capabilities are vendor claims, not independent proof of efficacy. |
How should a business choose between tools?
Start with the organization’s AI environment and threat model, not a product name. Map the systems in use and compare candidate controls against the work that needs to be done:
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- Locate the workloads. Identify which AI applications and agents run in cloud services, on endpoints, or in other environments. Check whether a tool can see the systems you actually use.
- Define the security job. Decide whether you need inventory and posture management, testing before deployment, protection while an agent runs, or some combination.
- Review identities and permissions. Confirm how agent identities are managed and whether the control helps enforce the access boundaries your threat model requires.
- Check integration and operations. Establish how alerts and findings fit existing monitoring, investigation, and incident-response workflows.
- Verify maturity and coverage. Confirm supported services and agents, product availability, licensing, deployment requirements, and data handling in current vendor documentation. Treat preview functionality differently from generally available capabilities.
A specialized platform is not automatically necessary for every business adopting agentic AI. Start with least privilege, identity management, layered defenses, threat modeling, monitoring, and regular assessment; add tooling where it addresses a defined visibility, testing, or runtime-control gap.
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