Key Takeaways

• AI security has two sides: using artificial intelligence to defend organizations and protecting AI systems themselves.
• Generative AI tools like ChatGPT, Microsoft Copilot, and internal copilots significantly expand the organizational attack surface.
• Attackers already use AI to automate phishing, fraud, vulnerability discovery, and social engineering.
• Shadow AI – employees using unsanctioned AI tools – creates serious data leakage and compliance risks.
• SMEs must treat AI systems as critical assets within cybersecurity programs.

What Is AI Security?

AI security refers to protecting AI systems, data, and workflows from manipulation, misuse, and data leaks while also using AI to strengthen cybersecurity defenses. AI and ML applications are increasingly used in cybersecurity to enhance threat detection, protect AI models, and address security challenges unique to AI deployments.

The term carries a dual meaning:

• AI for security: Using ML and AI powered capabilities inside SOC tools (SIEM, EDR, MXDR, SOAR) to achieve real time threat detection and automated response capabilities.
• Security of AI: Protecting AI models, training data, prompts, embeddings, and agent workflows from being attacked, manipulated, or misused. AI apps require robust security measures to prevent threats such as data poisoning, model theft, and misuse.

According to market forecasts, the AI security market is growing rapidly alongside overall AI adoption, driven by real incidents like prompt-injection breaches, model extraction attempts, and sensitive data leaks through public LLMs. For small and medium-sized businesses, AI security is a real concern – staff are already pasting source code, financials, and customer data into AI tools, often without any policy or oversight.

This uncontrolled use, known as shadow AI, dramatically expands the attack surface in ways that legacy security controls were never designed to address.

When AI Becomes Part of the Attack Surface

Consider an SME that deployed a customer support chatbot with an LLM backend in 2024. What started as a productivity enhancement now exposes a new web endpoint, a plugin ecosystem, and direct access to CRM data. Each integration point represents a potential pathway for threat actors to exploit.

An attack surface includes every point where attackers can interact with or influence organizational assets. For AI systems, this now encompasses:

• Prompts and user inputs
• Training data and fine-tuning corpora
• Plugins, tools, and API connections
• Third party components and models
• SaaS AI services and cloud systems

As AI systems integrate with more services, the number of hidden security dependencies grows rapidly. We explore these architectural risks in more detail in our article “5 Critical Security Risks Every AI-Native Company Must Address.”

How AI Systems Blur Traditional Security Boundaries

AI systems fundamentally change how security teams must think about perimeter defense. Traditional boundaries no longer apply when:

• AI agents can trigger actions across SaaS applications like Microsoft 365, Salesforce, and Jira
• LLMs can read internal documentation, source code repositories, and ticketing systems
• Misconfigurations can turn a helpful assistant into a powerful lateral movement tool for attackers

As organizations deploy more AI agents and integrations, each AI system effectively becomes a new non-human identity that requires secure authentication, monitoring, and access control.

AI in Modern Cybersecurity Defenses

While AI introduces new risks, it also significantly improves cyber defense capabilities. Machine learning has already transformed how security teams analyze data and detect threats.

Concrete uses of AI for security in recent years include:

• AI-driven anomaly detection spotting unusual access patterns, lateral movement, and data exfiltration
• Generative AI copilots inside SOC platforms that summarize incidents, draft response playbooks, and query historical data in natural language
• Automated correlation of IAM, cloud, and endpoint data to dramatically reduce false positives

These capabilities reduce alert fatigue and help security teams focus on the most critical threats.

Managed security services often combine these AI-driven tools with human analysts who validate alerts and coordinate incident response.

AI Adoption Inside SMEs’ Security Workflows

SMEs in sectors like fintech, health tech, and SaaS have rapidly adopted AI tools including Microsoft Security Copilot, GitHub Copilot, and cloud-native AI assistants by 2025–2026.

Real-world use cases include:

• Developers using AI to generate infrastructure-as-code and security configurations (learn more about the hidden risks of vibe coding)
• IT teams using LLMs to query security logs and policy documentation
• Non-technical staff using AI to triage phishing emails and suspicious messages

These productivity gains come with security risks. Model hallucinations can generate incorrect security configurations. Mis-configured IAM for AI assistants can grant excessive permissions. Unvetted actions suggested by AI can introduce vulnerabilities.

How Criminals Are Using AI Against You

Cybercriminals have rapidly adopted AI tools to improve the scale and effectiveness of cyber attacks. Generative AI allows attackers to automate tasks that previously required significant expertise.

Key Attacker Use Cases

Threat actors are leveraging AI across the attack chain:

• Mass-personalized phishing and smishing using scraped LinkedIn and social media data to create highly targeted lures
• Faster vulnerability research via code-assisting AI models that accelerate exploit development
• Automated reconnaissance of exposed assets, public code repos, and leaked data
• AI voice cloning and deepfake tools used in business email compromise and “CEO fraud” scams

The key difference AI introduces is scale. Attackers can now produce thousands of personalized phishing emails, automated vulnerability analysis, and large volumes of social engineering messages.

Core AI Security Risks: Data, Models, and Pipelines

Protecting AI systems requires addressing risks across three main areas: data, models, and infrastructure. Effective AI security requires understanding how risk manifests at each stage.

Data Risks

Data is the “source of truth” for AI. Poisoned or biased data corrupts everything built on top of it, making data security fundamental to AI security posture management.

Common data risks include:

• sensitive information shared in AI prompts
• exposure of internal documents through AI tools
• malicious manipulation of datasets
• unauthorized access to knowledge bases

Organizations should implement strong data classification policies and restrict what information can be processed by external AI services.

Model Risks

AI models themselves can be targeted by attackers. Key threats to model integrity include:

Prompt injection: Attackers craft inputs that manipulate the model into revealing hidden information or ignoring security instructions.

Model extraction: Repeated queries may allow attackers to approximate a model’s behavior or extract sensitive training data.

Adversarial inputs: Specially crafted data may cause AI systems to generate incorrect or harmful outputs.

Protection patterns include:

• Restricting model access via strong IAM, API keys, and network segmentation
• Rate limiting and anomaly detection on model queries and tool calls
• Output filtering and second-stage validation models to block harmful or policy-breaking responses
• Regular assessment of model behavior against expected outputs

Pipeline, Infrastructure, and Supply Chain Risks

AI systems rely on complex infrastructure including cloud services, APIs, and open-source components.

Security risks can arise from:

• compromised open-source libraries
• misconfigured cloud storage
• exposed APIs
• insecure deployment pipelines

Organizations must treat AI infrastructure as part of their overall attack surface and apply standard security practices such as vulnerability management and configuration monitoring.

When AI Misbehaves: Adversarial Attacks, Drift, and Misuse

AI systems are dynamic, with behavior that can change due to new data, evolving threats, or malicious manipulation. This ongoing evolution requires continuous security and assurance efforts.

Adversarial attacks use crafted inputs to cause incorrect or harmful outputs while appearing harmless. Examples include subtle image tweaks that bypass moderation, hidden prompts in documents, and deceptive audio samples. Defenses involve adversarial training, input sanitization, and conservative models for high-risk decisions.

Model drift and decay happen when changes in data or gradual performance loss reduce effectiveness. Attackers can exploit outdated models unless continuous monitoring, retraining, and clear incident response plans are in place.

Misuse and shadow AI – unapproved AI tools or integrations – pose risks like data leaks and security misconfigurations. Organizations should implement clear AI usage policies, conduct security training, and use technical controls to detect and manage unauthorized AI activity.

Governance and Compliance for AI

As AI adoption grows, regulatory frameworks are emerging to address risks related to transparency, accountability, and data protection.

Key regulatory and standards developments include:

• The EU AI Act
• data protection regulations such as GDPR (learn more about GDPR and AI: What SMEs Need to Know)
• industry-specific requirements in finance and healthcare

Organizations deploying AI should document how their systems use data, how decisions are made, and what safeguards exist.

Key governance practices include:

• documenting AI use cases
• conducting risk assessments
• defining accountability for AI decisions
• implementing monitoring and audit processes

Strong governance helps organizations manage risk while building trust with customers and partners.

AI Security Best Practices for SMEs

Most small and medium-sized organizations don’t have dedicated AI security engineers, yet they’re rapidly deploying AI features and AI tools. This gap between capability and risk creates vulnerability.

For resource-constrained SMEs, the focus is practical implementation: start where risk is highest, achieve measurable improvements, and build capability progressively.

Here are a few practical steps that can significantly reduce risk and secure AI systems:

1. Inventory AI systems

Maintain a record of AI security tools, integrations, and automation workflows used across the organization.

2. Establish AI usage policies

Define which AI services and AI models employees may use and what types of data can be shared with them. Specify requirements for on-premises deployment, VPN usage, and identity verification.

3. Control access

Apply least-privilege access controls to AI service accounts and integrations. Enforce MFA for all AI platform access. Segregate duties for AI agent permissions. Review access controls regularly.

4. Protect sensitive data

Use encryption, tokenization, and masking for sensitive fields in AI training. Implement DLP controls that specifically monitor AI-related data flows.

5. Monitor AI activity

Integrate AI-related logs and events into existing security monitoring systems.

6. Test and iterate

Run periodic red team and tabletop exercises focused on AI abuse scenarios including prompt injection and data exfiltration. Adjust controls based on findings.

AMATAS helps SMEs integrate AI security into their broader cybersecurity programs through vCISO guidance, managed monitoring, and security awareness initiatives.

If you’re evaluating AI security risks in your organization, you can schedule a free consultation with our team.

FAQ

Do small and mid-sized companies need AI-specific security?

Yes. Even organizations that only use public AI tools may expose sensitive data through prompts or integrations. Basic controls – such as usage policies, monitoring, and employee training – can significantly reduce risk.

Is using enterprise AI tools safer than public ones?

Enterprise AI platforms often include stronger privacy protections and administrative controls. However, organizations must still manage data access, permissions, and monitoring to ensure secure use.

How can organizations detect AI-enabled attacks?

AI-driven attacks often resemble traditional threats such as phishing or fraud but occur at greater scale and sophistication. Monitoring suspicious login activity, unusual data access patterns, and abnormal API usage can help identify potential incidents.

Where should organizations start with AI security?

A practical starting point is:

1. Identify AI tools already in use.
2. Define clear policies for acceptable use.
3. Monitor AI activity through existing security tools.
4. Conduct a risk assessment to identify priority controls.

Starting with visibility and governance allows organizations to expand their AI security capabilities over time.