AI Adoption in the Enterprise: Breaking Through the Security and Compliance Gridlock

April 3, 2025

AI holds the promise to revolutionize all sectors of enterpriseーfrom fraud detection and content personalization to customer service and security operations. Yet, despite its potential, implementation often stalls behind a wall of security, legal, and compliance hurdles.

Imagine this all-too-familiar scenario: A CISO wants to deploy an AI-driven SOC to handle the overwhelming volume of security alerts and potential attacks. Before the project can begin, it must pass through layers of GRC (governance, risk, and compliance) approval, legal reviews, and funding hurdles. This gridlock delays innovation, leaving organizations without the benefits of an AI-powered SOC while cybercriminals keep advancing.

Let's break down why AI adoption faces such resistance, distinguish genuine risks from bureaucratic obstacles, and explore practical collaboration strategies between vendors, C-suite, and GRC teams. We'll also provide tips from CISOs who have dealt with these issues extensively as well as a cheat sheet of questions AI vendors must answer to satisfy enterprise gatekeepers.

Compliance as the primary barrier to AI adoption

Security and compliance concerns consistently top the list of reasons why enterprises hesitate to invest in AI. Industry leaders like Cloudera and AWS have documented this trend across sectors, revealing a pattern of innovation paralysis driven by regulatory uncertainty.

When you dig deeper into why AI compliance creates such roadblocks, three interconnected challenges emerge. First, regulatory uncertainty keeps shifting the goalposts for your compliance teams. Consider how your European operations might have just adapted to GDPR requirements, only to face entirely new AI Act provisions with different risk categories and compliance benchmarks. If your organization is international, this puzzle of regional AI legislation and policies only becomes more complex. In addition, framework inconsistencies compound these difficulties. Your team might spend weeks preparing extensive documentation on data provenance, model architecture, and testing parameters for one jurisdiction, only to discover that this documentation is not portable across regions or is not up-to-date anymore. Lastly, the expertise gap may be the biggest hurdle. When a CISO asks who understands both regulatory frameworks and technical implementation, typically the silence is telling. Without professionals who bridge both worlds, translating compliance requirements into practical controls becomes a costly guessing game.

These challenges affect your entire organization: developers face extended approval cycles, security teams struggle with AI-specific vulnerabilities like prompt injection, and GRC teams who have the difficult task of safeguarding their organization take increasingly conservative positions without established benchmarks. Meanwhile, cybercriminals face no such constraints, rapidly adopting AI to enhance attacks while your defensive capabilities remain locked behind compliance reviews.

AI Governance challenges: Separating myth from reality

With so much uncertainty surrounding AI regulations, how do you distinguish real risks from unnecessary fears? Let's cut through the noise and examine what you should be worrying about—and what you can let be. Here are some examples:

FALSE: "AI governance requires a whole new framework."

Organizations often create entirely new security frameworks for AI systems, unnecessarily duplicating controls. In most cases, existing security controls apply to AI systems—with only incremental adjustments needed for data protection and AI-specific concerns.

TRUE: "AI-related compliance needs frequent updates."

As the AI ecosystem and underlying regulations keep shifting, so does AI governance. While compliance is dynamic, organizations can still handle updates without overhauling their entire strategy.

FALSE: "We need absolute regulatory certainty before using AI."

Waiting for complete regulatory clarity delays innovation. Iterative development is key, as AI policy will continue evolving, and waiting means falling behind.

TRUE: "AI systems need continuous monitoring and security testing."

Traditional security tests don't capture AI-specific risks like adversarial examples and prompt injection. Ongoing evaluation—including red teaming—is critical to identify bias and reliability issues.

FALSE: "We need a 100-point checklist before approving an AI vendor."

Demanding a 100-point checklist for vendor approval creates bottlenecks. Standardized evaluation frameworks like NIST's AI Risk Management Framework can streamline assessments.

TRUE: "Liability in high-risk AI applications is a big risk."

Determining accountability when AI errors occur is complex, as errors can stem from training data, model design, or deployment practices. When it's unclear who is responsible—your vendor, your organization, or the end-user—careful risk management is necessary.

Effective AI governance should prioritize technical controls that address genuine risks—not create unnecessary roadblocks that keep you stuck while others move forward.

The way forward: Driving AI innovation with Governance

Organizations that adopt AI governance early gain significant competitive advantages in efficiency, risk management, and customer experience over those that treat compliance as a separate, final step.

Take JPMorgan Chase's AI Center of Excellence (CoE) as an example. By leveraging risk-based assessments and standardized frameworks through a centralized AI governance approach, they've streamlined the AI adoption process with expedited approvals and minimal compliance review times.

Meanwhile, for organizations that delay implementing effective AI governance, the cost of inaction grows daily:

Increased security risks: Without AI-powered security solutions, your organization becomes increasingly vulnerable to sophisticated, AI-driven cyber attacks that traditional tools cannot detect or mitigate effectively.
Lost opportunities: Failing to innovate with AI results in lost opportunities for cost savings, process optimization, and market leadership as competitors leverage AI for competitive advantage.
Regulatory debt: Future tightening of regulations will increase compliance burdens, forcing rushed implementations under less favorable conditions and potentially higher costs.
Inefficient late adoption: Retroactive compliance often comes with less favorable terms, requiring substantial rework of systems already in production.

Balancing governance with innovation is critical: as competitors standardize AI-powered solutions, you can ensure your market share through more secure, efficient operations and enhanced customer experiences powered by AI and future-proofed through AI governance.

How can vendors, executives and GRC teams work together to unlock AI adoption?

AI adoption works best when your security, compliance, and technical teams collaborate from day one. Based on conversations we've had with CISOs, we'll break down the top three key governance challenges and offer practical solutions.

Who should be responsible for AI Governance in your organization?

Answer: Create shared accountability through cross-functional teams: CIOs, CISOs, and GRC can work together within an AI Center of Excellence (CoE).

As one CISO candidly told us: "GRC teams get nervous when they hear 'AI' and use boilerplate question lists that slow everything down. They're just following their checklist without any nuance, creating a real bottleneck."

What organizations can do in practice:

Form an AI governance committee with people from security, legal, and business.
Create shared metrics and language that everyone understands to track AI risk and value.
Set up joint security and compliance reviews so teams align from day one.

How can vendors make data processing more transparent?

Answer: Build privacy and security into your design from the ground up so that common GRC requirements are already addressed from day 1.

Another CISO was crystal clear about their concerns: "Vendors need to explain how they'll protect my data and whether it will be used by their LLM models. Is it opt-in or opt-out? And if there's an accident—if sensitive data is accidentally included in the training—how will they notify me?"

What organizations acquiring AI solutions can do in practice:

Use your existing data governance policies instead of creating brand-new structures (see next question).
Build and maintain a simple registry of your AI assets and use cases.
Make sure your data handling procedures are transparent and well-documented.
Develop clear incident response plans for AI-related breaches or misuse.

Are existing exemptions to privacy laws also applicable to AI tools?

Answer: Consult with your legal counsel or privacy officer.

That said, an experienced CISO in the financial industry explained, "There is a carve out within the law for processing private data when it's being done for the benefit of the customer or out of contractual necessity. As I have a legitimate business interest in servicing and protecting our clients, I may use their private data for that express purpose and I already do so with other tools such as Splunk." He added, "This is why it's so frustrating that additional roadblocks are thrown up for AI tools. Our data privacy policy should be the same across the board."

How can you ensure compliance without killing innovation?

Answer: Implement structured but agile governance with periodic risk assessments.

One CISO offered this practical suggestion: "AI vendors can help by proactively providing answers to common questions and explanations for why certain concerns aren't valid. This lets buyers provide answers to their compliance team quickly without long back-and-forths with vendors."

What AI vendors can do in practice:

Focus on the "common ground" requirements that appear in most AI policies.
Regularly review your compliance procedures to cut out redundant or outdated steps.
Start small with pilot projects that prove both security compliance and business value.

7 questions AI vendors need to answer to get past enterprise GRC teams

At Radiant Security, we understand that evaluating AI vendors can be complex. Over numerous conversations with CISOs, we've gathered a core set of questions that have proven invaluable in clarifying vendor practices and ensuring robust AI governance across enterprises.

1. How do you ensure our data won't be used to train your AI models?

"By default, your data is never used for training our models. We maintain strict data segregation with technical controls that prevent accidental inclusion. If any incident occurs, our data lineage tracking will trigger immediate notification to your security team within 24 hours, followed by a detailed incident report."

2. What specific security measures protect data processed by your AI system?

"Our AI platform uses end-to-end encryption both in transit and at rest. We implement strict access controls and regular security testing, including red team exercises; we also maintain SOC 2 Type II, ISO 27001, and FedRAMP certifications. All customer data is logically isolated with strong tenant separation."

3. How do you prevent and detect AI hallucinations or false positives?

"We implement multiple safeguards: retrieval augmented generation (RAG) with authoritative knowledge bases, confidence scoring for all outputs, human verification workflows for high-risk decisions, and continuous monitoring that flags anomalous outputs for review. We also conduct regular red team exercises to test the system under adversarial conditions."

4. Can you demonstrate compliance with regulations relevant to our industry?

"Our solution is designed to support compliance with GDPR, CCPA, NYDFS, and SEC requirements. We maintain a compliance matrix mapping our controls to specific regulatory requirements and undergo regular third-party assessments. Our legal team tracks regulatory developments and provides quarterly updates on compliance enhancements."

5. What happens if there's an AI-related security breach?

"We have a dedicated AI incident response team with 24/7 coverage. Our process includes immediate containment, root cause analysis, customer notification within contractually agreed timeframes (typically 24-48 hours), and remediation. We also conduct tabletop exercises quarterly to test our response capabilities."

6. How do you ensure fairness and prevent bias in your AI systems?

"We implement a comprehensive bias prevention framework that includes diverse training data, explicit fairness metrics, regular bias audits by third parties, and fairness-aware algorithm design. Our documentation includes detailed model cards that highlight limitations and potential risks."

7. Will your solution play nicely with our existing security tools?

"Our platform offers native integrations with major SIEM platforms, identity providers, and security tools through standard APIs and pre-built connectors. We provide comprehensive integration documentation and dedicated implementation support to ensure seamless deployment."

Bridging the gap: AI innovation meets Governance

AI adoption isn't stalled by technical limitations anymore—it's delayed by compliance and legal uncertainties. But AI innovation and governance aren't enemies. They can actually strengthen each other when you approach them right.

Organizations that build practical, risk-informed AI governance aren't just checking compliance boxes but securing a real competitive edge by deploying AI solutions faster, more securely, and with greater business impact. For your security operations, AI may be the single most important differentiator in future-proofing your security posture.

While cybercriminals are already using AI to enhance their attacks' sophistication and speed, can you afford to fall behind? Making this work requires real collaboration: Vendors must address compliance concerns proactively, C-suite executives should champion responsible innovation, and GRC teams need to transition from gatekeepers to enablers. This partnership unlocks AI's transformative potential while maintaining the trust and security that customers demand.

About Radiant Security

Radiant Security provides an AI-powered SOC platform designed for SMB and enterprise security teams looking to fully handle 100% of the alerts they receive from multiple tools and sensors. Ingesting, understanding, and triaging alerts from any security vendor or data source, Radiant ensures no real threats are missed, cuts response times from days to minutes, and enables analysts to focus on true positive incidents and proactive security. Unlike other AI solutions which are constrained to predefined security use cases, Radiant dynamically addresses all security alerts, eliminating analyst burnout and the inefficiency of switching between multiple tools. Additionally, Radiant delivers affordable, high-performance log management directly from customers' existing storage, dramatically reducing costs and eliminating vendor lock-in associated with traditional SIEM solutions.

Learn more about the leading AI SOC platform.

About Author: Shahar Ben Hador spent nearly a decade at Imperva, becoming their first CISO. He went on to be CIO and then VP Product at Exabeam. Seeing how security teams were drowning in alerts while real threats slipped through, drove him to build Radiant Security as co-founder and CEO.