As businesses integrate AI tools across data analytics, customer service, and autonomous operations, these systems become high-value targets for threat actors. Darktrace's State of AI Cybersecurity 2026 report found that 87% of security leaders believe AI is leading to a greater number of significant threats that demand their attention, while 92% are concerned with how the expansion of AI agents across the workforce may affect security. IT and cybersecurity teams face the challenge of securing complex, often opaque AI ecosystems that traditional security tools were not designed to protect.

This guide provides a technical breakdown of securing AI tools, including the unique threats and risks associated with artificial intelligence or machine learning (AI/ML) systems, essential considerations for responsible AI adoption, and how AI-driven defense capabilities can support AI security when deployed effectively.

What threats and risks are associated with AI tools?

Securing generative AI tools requires understanding attack techniques that target the unique logic and data pipelines of AI/ML systems. Traditional security approaches focus on protecting code and infrastructure, but threats targeting AI systems exploit the models themselves and the data that powers them.

AI/ML systems face four primary attack categories:

Data poisoning

Data poisoning occurs when adversaries corrupt training datasets to manipulate a model's behavior. By introducing malicious or biased data during training, adversaries can cause models to produce flawed outputs that serve their objectives.

A poisoned fraud detection model might learn to classify fraudulent transactions as legitimate, enabling financial theft at scale. In autonomous systems, poisoned training data could cause vehicles to misidentify stop signs or pedestrians.

Model evasion

Model evasion techniques allow adversaries to craft malicious inputs that are intentionally misclassified by AI systems. Designed inputs exploit weaknesses in model decision boundaries, causing the system to produce incorrect outputs.

Adversaries might modify malware binaries with subtle changes that preserve malicious functionality while causing AI-powered security scanners to classify files as benign, or evade spam filters through strategic word substitutions. Evasion attacks are particularly dangerous because they occur during inference, when models make decisions in production without human review.

Model and data extraction

Model extraction and data extraction represent two related attack types targeting intellectual property and sensitive information.

• Model stealing: Involves reverse-engineering proprietary algorithms through carefully crafted queries to the model's API. Adversaries submit inputs to probe decision boundaries and analyze outputs to build a functionally equivalent model, stealing years of research investment without accessing model weights or training code.
• Data extraction: These attacks aim to reconstruct sensitive training data by exploiting model responses. Language models can be prompted to output memorized training examples verbatim, potentially exposing confidential business information or proprietary content.

Prompt injection

Prompt injection has emerged as a critical challenge for organizations working to secure gen AI systems. The Open Worldwide Application Security Project Top 10 for Large Language Model Applications ranks prompt injection as the leading security risk for Large Language Model (LLM) deployments.

Malicious prompts override an LLM's safety instructions, causing it to bypass content filters or generate harmful outputs. This vulnerability exploits how LLMs process instructions and user input within the same context window without reliable separation between system commands and untrusted data.

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Securing enterprise AI is a complex discipline that cannot be comprehensively addressed in a single guide. The following represents key areas to keep in mind when approaching responsible AI adoption. These are not exhaustive solutions but critical elements that should inform your broader security planning.

Establish governance before deployment

Effective AI security begins with clear governance frameworks that define acceptable use, data handling requirements, and accountability structures. Organizations should consider:

• Policy frameworks: Document acceptable AI use cases, prohibited applications, and escalation procedures for emerging risks before systems reach production.
• Stakeholder alignment: Ensure security, legal, compliance, and business units agree on risk tolerance and decision-making authority for AI deployments.
• Regulatory awareness: Map your AI security controls to requirements in relevant standards like the European Union AI Act to demonstrate compliance with transparency and risk management obligations.

Prioritize data integrity and privacy

AI systems are only as trustworthy as the data that trains them. Security teams should maintain awareness of:

• Data provenance: Understand where training data originates and maintain documentation that traces datasets back to verified sources.
• Privacy-preserving techniques: Consider approaches that analyze metadata rather than sensitive content, and ensure models are trained on privacy-preserving datasets when handling regulated information.
• Validation mechanisms: Implement checks that flag anomalies in training data before models reach production, reducing exposure to data poisoning attacks.

Build interpretability and transparency

AI systems that operate as black boxes undermine security oversight and complicate incident response. Organizations should consider:

• Explainable outputs: Deploy systems that can contextualize alerts and decisions, enabling security teams to understand the reasoning behind model behavior.
• Audit capabilities: Maintain detailed logs of model interactions, including training runs with dataset identifiers, inference requests, and configuration changes to enable forensic investigation.
• Human oversight mechanisms: Establish clear boundaries for where AI can act autonomously and where human review is required, particularly for high-risk decisions.

Test for security and robustness

AI systems introduce new attack surfaces that require ongoing evaluation. Security teams should keep in mind:

• Adversarial testing: Conduct regular testing to identify how models respond to malicious inputs, evasion techniques, and extraction attempts.
• Continuous monitoring: Track model behavior against established baselines to identify drift that may indicate compromise, degradation, or unintended bias.
• Incident response planning: Develop procedures for responding to AI-specific incidents, including model rollback capabilities and contaminated data handling protocols.

AI and insider threat risk are increasingly connected as organizations adopt generative AI tools and autonomous agents, appearing in three areas that security teams now need to account for. It expands what malicious insiders can do, turns AI agents themselves into possible insiders, and increases the potential scale of accidental data exposure by well-meaning employees.

AI as a cognitive amplifier for deliberate insiders

The impact of AI on insider threat risk is especially clear when generative tools are used to plan or refine harmful activity. Employees considering insider actions may use generative AI systems to refine strategies, identify weaknesses, or explore ways to avoid detection. Because generative platforms are designed to respond to user intent, they can inadvertently make insider activity easier to research or operationalize. This lowers the technical barrier for insiders attempting more sophisticated activity.

Nonhuman identities as autonomous insiders

When organizations deploy agents with delegated credentials and broad system access, these agents operate with minimal human oversight. Darktrace research demonstrated that agents given impossible tasks independently may turn to hacking methods to achieve objectives, with no instruction to do so.

Once compromised, agents can act within the environment faster than a human user can. Unlike human insiders whose actions may unfold across hours or days, an agent can traverse systems, escalate privileges, and exfiltrate data rapidly once operational constraints are bypassed.

Acceleration of negligent insider data exposure

AI can also increase the risk posed by negligent insiders. Employees using AI assistants to improve productivity may inadvertently expose regulated data via natural language prompts or file uploads to generative platforms, unaware of the security implications. A single accidental disclosure can now scale to repeated exposure patterns as AI tools become embedded in daily workflows.

Organizations face compounded risk from shadow AI adoption, in which employees use unsanctioned AI tools operating outside organizational monitoring, creating visibility gaps that both negligent and malicious insiders exploit. Security teams now need to monitor insider risk across both human users and nonhuman identities, including the speed, permissions, and data access patterns of AI agents.

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Enterprise AI security works best when security teams can see where AI is used, control what it can access, monitor how it behaves, and manage risks before deployment. These best practices cover the core layers of AI platform security for enterprises.

Establish governance and visibility

Organizations need governance and visibility in place before AI systems or agents reach production. This foundation includes documenting acceptable AI use cases, defining data handling requirements, and assigning clear accountability for AI system behavior. Security teams must maintain comprehensive visibility into AI deployments, including shadow AI usage, models, agents, and AI-enabled SaaS applications across the enterprise. Mapping these controls to AI governance frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework can help teams document maturity and plan for compliance.

Implement identity and access controls

Identity and access management is a critical control layer for AI agents. Organizations should assign distinct identities to agents rather than allowing them to share human or service account credentials. Apply least-privilege access controls with just-in-time permissions, and treat high-access AI agents as privileged identities. This includes continuous monitoring for permission drift, credential misuse, and actions inconsistent with the agent's intended business purpose.

Deploy runtime security and behavioral monitoring

Monitor how agents behave after deployment with runtime security, rather than relying on prompt filters alone. Start by baselining normal agent activity, so your team can spot unusual behavior that may signal compromise or misalignment. While guardrails can help with known attack patterns, they may miss novel behavior, goal drift, or agents pursuing objectives in unintended ways. Behavior-based detection helps close that gap by identifying activity that falls outside an agent’s expected role or purpose.

Darktrace research on rogue agent behavior identified misaligned agent activity, while autonomous response disrupted progression in the early stages.

Secure development and supply chain

Secure AI systems before they reach production by reviewing agent designs, checking AI-generated code, and monitoring configurations throughout the build process. Supply chain controls should also verify where models and training data come from, how third-party suppliers handle enterprise information, and which external AI agents can access internal systems. AI security frameworks should also account for incident response. That means having clear procedures for rolling back models, containing agents, and investigating the behavior of autonomous systems when something goes wrong.

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