Breaking Through the Security and Compliance Gridlock

Artificial Intelligence (AI) is poised to transform every facet of enterprise operations, from fraud detection to customer service. However, the journey to AI implementation often confronts significant barriers related to security, legal frameworks, and compliance protocols.

Picture this common situation: a Chief Information Security Officer (CISO) eager to roll out an AI-powered Security Operations Center (SOC) to manage the flood of security alerts faces a lengthy approval process filled with governance, risk management, and compliance (GRC) hurdles. Each delay not only hampers innovation but also leaves organizations vulnerable to the evolving tactics of cybercriminals.

In this article, we’ll explore the roots of the resistance to AI adoption, demystify concerns related to compliance, and outline collaboration strategies that bring together vendors, executives, and GRC teams. We’ll also share insights from experienced CISOs and create a checklist of essential questions that AI vendors must address to reassure anxious enterprise gatekeepers.

Compliance: A Major Obstacle to AI Adoption

Security and compliance fears are consistently at the forefront of enterprises’ hesitations towards AI investments. Companies like Cloudera and AWS have identified this trend, noting that regulatory ambiguities foster a culture of innovation paralysis.

When we delve deeper into AI compliance issues, three significant challenges arise. Firstly, regulatory frameworks are constantly changing, which creates a moving target for compliance teams. Imagine your European division adapting to GDPR, only to face looming AI Act guidelines involving different regulatory standards. This problem multiplies for multinational organizations aware of diverse regional legislation. Secondly, inconsistencies among regulatory frameworks complicate matters, as extensive compliance documentation prepared for one community may not apply elsewhere. Finally, a significant skills gap persists: when a CISO asks about professionals who can interpret both regulatory frameworks and technical applications, silence typically follows. Without a bridge between compliance specialists and tech experts, navigating legal requirements becomes a game of guesswork.

These barriers impact every layer of the organization. Developers endure prolonged approval processes, security teams grapple with AI-specific vulnerabilities like prompt injection, while GRC teams lean towards overly cautious strategies in the absence of clear benchmarks. Meanwhile, cybercriminals are unfettered, leveraging AI to enhance their attacks while organizations stay bogged down by red tape.

AI Governance: Sorting Fact from Fiction

With the ambiguity surrounding AI regulations, it’s crucial to differentiate between authentic risks and unfounded fears. Here’s a closer look:

MISCONCEPTION: “AI governance necessitates a brand-new framework.”

Companies often attempt to build entire frameworks for AI, duplicating existing controls unnecessarily. In reality, modifications to current security measures can suffice.

TRUTH: “Compliance for AI systems requires regular updates.”

AI governance can evolve alongside changes in the regulatory landscape. Organizations don’t need to overhaul their entire compliance strategy to adapt.

MISCONCEPTION: “We should wait for absolute regulatory clarity before utilizing AI.”

Halting innovation in anticipation of complete regulatory clarity can lead to missed opportunities. Iterative development remains essential in a fast-evolving AI policy scenario.

TRUTH: “AI systems necessitate ongoing monitoring and security testing.”

Conventional security assessments often overlook AI’s unique risks. Continuous evaluations, including red team strategies, are crucial for identifying potential biases and reliability issues.

MISCONCEPTION: “A comprehensive checklist is necessary for vendor approval.”

Requiring lengthy vendor checklists can create significant bottlenecks. Instead, leveraging standardized evaluation frameworks like NIST’s AI Risk Management Framework streamlines assessments.

TRUTH: “Liability in high-risk AI applications represents a substantial risk.”

Pinpointing responsibility in the event of AI errors can be challenging—issues may arise from data, model design, or deployment strategies. Effective risk management is essential to clarify accountability.

Adopting an efficient AI governance model should focus on genuine risks to avoid unnecessary obstacles while fostering an environment conducive to technological advancement.

Moving Forward: Propelling AI Innovation through Governance

Organizations that integrate AI governance early on enjoy critical advantages over those treating compliance as an afterthought—enhanced efficiency, superior risk management, and optimized customer experiences.

For example, JPMorgan Chase created an AI Center of Excellence (CoE) that employs risk-based assessments within a centralized governance approach, leading to swift approvals and streamlined compliance reviews.

Conversely, organizations that postpone effective AI governance face escalating costs tied to inaction:

• Heightened security risks: Without AI-enhanced security solutions, organizations become more exposed to advanced cyber threats traditional methods struggle to counter.
• Missed opportunities: Hesitation to innovate with AI may lead to disadvantages in cost reduction, operational efficiency, and competitive positioning as rivals harness AI’s potential.
• Regulatory debt: Future intensification of regulations may heighten compliance demands, forcing rushed implementations under suboptimal conditions.
• Delayed adoption: Retroactive compliance often results in less advantageous terms, necessitating substantial rework of existing systems.

Striking the right balance between governance and innovation is vital. As competitors move forward with AI, organizations can secure their market presence through enhanced, secure operations and improved customer interactions driven by AI governance.

How Vendors, Executives, and GRC Teams Can Collaborate to Propel AI Adoption

The most successful AI initiatives arise from seamless collaboration among security, compliance, and technical teams from the outset. Drawing from discussions with CISOs, we present three key governance challenges along with practical solutions.

Who Should Oversee AI Governance in Your Organization?

Answer: Establish shared accountability through interdisciplinary teams: CIOs, CISOs, and GRC can collaborate within an AI Center of Excellence (CoE).

As one CISO shared, “GRC teams often panic when they hear ‘AI’ and typically apply generic question lists that slow progress. They stick to checklists, creating real bottlenecks.”

Practical Steps:

• Form an AI governance committee with representatives from security, legal, and business.
• Create shared metrics and language for tracking AI risk and value.
• Implement joint security and compliance reviews to ensure alignment from the start.

How Can Vendors Enhance Data Processing Transparency?

Answer: Incorporate privacy and security into designs from day one, addressing common GRC requirements proactively.

Another CISO emphasized, “Vendors should clarify how they will protect our data and whether it will be included in their models. Is there an option to opt-out? What happens if sensitive data accidentally makes it into training?”

Practical Steps for Organizations:

• Apply existing data governance policies instead of reinventing the wheel.
• Create and maintain a clear registry of AI assets and their applications.
• Document and clarify data handling procedures.
• Formulate incident response plans for AI-related breaches.

Are Existing Exemptions to Privacy Laws Applicable for AI Tools?

Answer: Collaborate with legal counsel or a privacy officer for specific guidance.

A seasoned CISO remarked, “There’s often a carve-out within privacy laws for processing private data when it’s necessary for client service. This is also true for traditional tools like Splunk, making it irritating that new hurdles arise for AI tools.”

How Can You Ensure Compliance Without Stifling Innovation?

Answer: Adopt structured yet adaptable governance practices, including regular risk evaluations.

One CISO suggested, “AI vendors should preemptively answer common questions and debunk misconceptions to expedite compliance discussions.”

What AI Vendors Can Do:

• Concentrate on requirements that align with various AI policies.
• Periodically reassess compliance procedures to eliminate redundant steps.
• Start small with pilot projects that demonstrate security compliance alongside business efficacy.

Key Questions for AI Vendors to Satisfy Enterprise GRC Teams

At Radiant Security, we know that evaluating AI vendors can be challenging. Through discussions with CISOs, we’ve compiled crucial questions that can clarify vendor practices and ensure comprehensive AI governance within enterprises.

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

“Our default policy is that your data is never used for training our models. We enforce strict data segregation with technical controls ensuring accidental inclusion is impossible. In the event of an incident, our data lineage will trigger immediate notification to your security team within 24 hours.”

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

“Our AI platform offers end-to-end encryption while data is both in transit and at rest. We utilize rigorous access controls and continual security evaluations, including red team exercises. Additionally, we maintain SOC 2 Type II, ISO 27001, and FedRAMP certifications, ensuring strong tenant separation for customer data.”

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

“We implement safeguards such as retrieval augmented generation (RAG) combined with authorized knowledge bases, confidence scoring for all outputs, human verification workflows for significant decisions, and continuous monitoring to flag unusual results for review. Regular red team exercises evaluate our system’s integrity under adversarial conditions.”

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

“Our solution is structured to comply with regulations including GDPR, CCPA, NYDFS, and SEC guidelines. We maintain a compliance matrix outlining our controls in relation to specific regulatory requirements and engage in regular third-party assessments. Our legal team consistently tracks regulatory changes and provides quarterly compliance updates.”

5. What happens in the event of an AI-related security breach?

“We possess a dedicated AI incident response team available 24/7. Our strategy encompasses swift containment, root cause identification, client notification timely per our agreements (typically within 24-48 hours), and remediation. We routinely conduct exercises to test our response plans.”

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

“Our bias mitigation framework encompasses diverse training datasets, clear fairness metrics, regular external audits for bias assessments, and fairness-oriented algorithm designs. Detailed documentation, including model cards, highlights limitations and potential risks.”

7. Will your solution integrate smoothly with our existing security tools?

“Our platform is designed for seamless compatibility with major SIEM platforms, identity solutions, and security tools through standard APIs and pre-built connectors. We offer thorough integration documentation and dedicated support to facilitate smooth deployment.”

Bridging the Gap: AI Innovation Meets Governance

The impediment to AI adoption is increasingly tied to regulatory uncertainties rather than technological limitations. However, AI innovation and governance can coexist harmoniously—each reinforcing the other when approached collaboratively.

Organizations that emphasize practical, risk-informed AI governance aren’t merely ticking compliance boxes; they’re securing a competitive edge by swiftly implementing AI solutions with security in focus, yielding substantial business benefits. AI may be the key differentiator in fortifying your security posture moving forward.

As adversaries exploit AI to elevate their attack capabilities, can your organization afford to lag behind? Success hinges on authentic collaboration: vendors must proactively address compliance concerns, executives must advocate for responsible innovation, and GRC teams should evolve from restrictive gatekeepers to partners in progress. Together, we can unlock the transformative potential of AI while maintaining customer trust and security.

About Radiant Security

Radiant Security offers an AI-driven SOC platform tailored for both small and large security teams needing to manage 100% of the alerts generated by their various tools and sensors. By efficiently processing and triaging alerts from any security vendor or data source, Radiant ensures that no genuine threats go unnoticed, slashes response times from days to minutes, and enables analysts to focus on significant incidents. Unlike other AI tools limited to specific use cases, Radiant dynamically addresses a wide range of security alerts, alleviating analyst fatigue and streamlining operational efficiency. Furthermore, Radiant provides cost-effective, high-performance log management directly from customers’ storage, significantly lowering expenses and bypassing the vendor lock-ins that often accompany traditional SIEM solutions.

Discover more about our cutting-edge AI SOC platform.

About the Author: Shahar Ben Hador brings nearly ten years of experience, including being the first CISO at Imperva, followed by leadership roles at Exabeam, where his vision was shaped by witnessing security teams overwhelmed by alerts while missing critical threats, leading him to co-found and lead Radiant Security as CEO.