AI Security Services

Artificial intelligence is the fastest-growing attack surface in the enterprise — and most organizations are deploying AI tools faster than their security controls can keep pace.

AI Security Services

Artificial intelligence is the fastest-growing attack surface in the enterprise — and most organizations are deploying AI tools faster than their security controls can keep pace. In 2025, NIST tracked a greater than 2,000% increase in AI-specific CVEs since 2022. Gartner identified AI-specific threats as the number one emerging risk category for enterprises. And 68% of organizations have already experienced data leaks linked to AI tool usage — yet only 23% have formal security policies governing how employees use AI.

Cyber Security Services helps you harness AI confidently and responsibly. Our AI Security practice is built on NIST AI RMF (AI 100-1), ISO/IEC 42001, and OWASP Top 10 for LLM Applications — giving you a governance-first, risk-driven approach to securing every AI system in your environment.

>2,000%

Increase in AI-specific CVEs tracked by NIST since 2022, as attackers systematically target AI systems and LLM deployments. (NIST NVD / Practical DevSecOps, 2026)

68%

Of organizations have experienced data leaks linked to AI tool usage — but only 23% have formal AI security policies in place. (Metomic, 2025)

$5.72M

Average cost of an AI-powered data breach in 2025 — 13% higher than a conventional breach, as AI-driven attacks move faster and are harder to contain. (SQ Magazine, 2025)

Why AI Security Cannot Wait

The threat is evolving faster than most security teams realize. In 2025, 41% of active ransomware families incorporated AI components for adaptive payload delivery. LLMs like open-source GPT variants were used to craft 91% of detected spear-phishing campaigns. Autonomous ransomware capable of lateral movement without human oversight appeared in 19% of breaches. AI-powered DDoS attacks reached a record 2.1 million unique incidents.

At the same time, the AI tools your employees are using today — Microsoft Copilot, ChatGPT Enterprise, Salesforce Einstein, custom LLM deployments — connect directly to your most sensitive data: customer records, financial information, source code, PHI, and legal documents. Without security controls and governance policies, those tools are moving data outside the boundaries your existing DLP and compliance controls were designed to protect.

Our AI Security Services

AI Risk Assessment

We evaluate your AI systems, data pipelines, and third-party AI tools for security, privacy, and compliance risks. Our assessment covers model inputs and outputs, training data handling and data governance, inference environment exposure, API security, integration points with enterprise systems, and employee AI usage patterns (including shadow AI). You receive a risk-prioritized findings report mapped to NIST AI RMF and ISO 42001 controls.

AI Governance Program Development

We build your organization’s AI governance framework from the ground up — or assess and mature an existing one. This includes acceptable use policies, AI system inventory and classification, risk threshold definitions, human oversight control design, vendor AI due diligence processes, and accountability structures for AI-assisted decision making. Our governance frameworks are designed to satisfy both NIST AI RMF (Govern, Map, Measure, Manage) and ISO/IEC 42001 management system requirements, positioning you for ISO 42001 certification if desired.

LLM & Generative AI Security

Large language models introduce a class of vulnerabilities that traditional security controls were not designed to address. Prompt injection holds the number one spot on the OWASP Top 10 for LLM Applications 2025. We assess your LLM deployments — whether internally hosted, vendor-provided, or API-connected — for the full OWASP LLM Top 10, including:

Prompt injection and indirect prompt injection via external data sources
Sensitive data leakage through AI-generated outputs
Insecure output handling and downstream system exploitation
Training data poisoning and model integrity risks
Supply chain vulnerabilities in AI tooling and model providers
Excessive agency — AI agents with overprivileged access to enterprise systems
Shadow AI and unmanaged employee AI tool usage

Agentic AI Security

Autonomous AI agents represent the most consequential unsecured asset in the modern enterprise. OpenAI and Google DeepMind both identified agentic AI systems as their number one near-term safety concern. Researchers have demonstrated that compromised AI agents can exfiltrate data, escalate privileges, and traverse networks without any human interaction — and 80% of current enterprise security stacks are entirely unprepared to detect this activity. We assess your agentic AI deployments for privilege boundaries, data access scope, action authorization controls, and logging requirements.

AI Regulatory Compliance

AI compliance requirements are arriving rapidly across every industry:

EU AI Act (GPAI model obligations effective August 2025) — high-risk AI classification, transparency requirements, and human oversight mandates
HIPAA — AI systems processing PHI or making AI-assisted clinical decisions are subject to Security Rule and Privacy Rule requirements
SEC — AI used in financial services decision making faces increasing regulatory scrutiny under existing FINRA/SEC frameworks
CMMC — AI tools used in defense contracting environments must satisfy applicable CMMC security requirements
State AI regulations — Colorado, Texas, and California have enacted or are enacting AI-specific requirements for automated decision-making systems

AI Security Awareness Training

Your employees are your first line of defense against AI-specific threats — and your greatest source of AI-related risk. We deliver security awareness training specifically focused on responsible AI usage: recognizing AI-generated phishing (which shows significantly higher engagement rates than traditional phishing), safe prompt hygiene for enterprise AI tools, recognizing and reporting shadow AI, and understanding what data must never enter an AI system.

The NIST AI RMF + ISO 42001 Approach

Our AI Security practice is built on the two most authoritative AI governance frameworks available today. The NIST AI RMF (AI 100-1) provides a flexible, risk-based approach organized around four core functions: Govern, Map, Measure, and Manage. ISO/IEC 42001 provides a certifiable management system standard with structured requirements for establishing, implementing, and continuously improving AI governance.

Together, they form the foundation of a comprehensive, auditable AI security program that satisfies U.S. regulatory expectations and international standards simultaneously.

Frequently Asked Questions

Does my company need AI security even if we only use third-party tools like Microsoft Copilot?

Yes. Using a vendor-provided AI tool does not transfer your security or compliance responsibility. Your organization remains accountable for what data flows into those systems, what permissions AI tools are granted, how AI-generated outputs are used in business decisions, and whether AI usage satisfies your HIPAA, PCI DSS, or other compliance obligations. Many existing DLP and compliance controls have significant blind spots for AI tool usage — we help you close them.

What is shadow AI, and why does it matter?

Shadow AI refers to employees independently adopting and using AI tools that have not been reviewed, approved, or governed by IT or security. It is the AI-era version of shadow IT — but the consequences are faster and harder to detect. An employee pasting customer records into an unapproved AI chatbot creates a data governance failure the moment it happens, regardless of whether a breach occurs. Our AI risk assessments surface shadow AI usage across your environment.

What is prompt injection, and how do you defend against it?

Prompt injection occurs when malicious instructions are embedded in content that an LLM processes — causing the model to ignore its intended instructions and execute attacker-controlled commands instead. It is the number one vulnerability on OWASP’s LLM Top 10. Defense requires input validation, output sandboxing, privilege separation between AI agents and enterprise systems, and monitoring for anomalous model behavior — controls we design and implement as part of our LLM security assessments.

AI Security Services

AI Security Services

>2,000%

68%

$5.72M

Why AI Security Cannot Wait

Our AI Security Services

The NIST AI RMF + ISO 42001 Approach

Frequently Asked Questions

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